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Create README.md
2023-03-10 19:47:46 +00:00
# llama.cpp
readme : change logo + add bindings + add uis + add wiki
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![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png)
Add logo to README.md
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Update README.md
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[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
readme : add project status link
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[Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
readme : add new roadmap + manifesto
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README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
Create README.md
2023-03-10 19:47:46 +00:00
gguf : new file format with flexible meta data (beta) (#2398) * gguf : first API pass * gguf : read header + meta data * gguf : read tensor info * gguf : initial model loading - not tested * gguf : add gguf_get_tensor_name() * gguf : do not support passing existing ggml_context to gguf_init * gguf : simplify gguf_get_val * gguf : gguf.c is now part of ggml.c * gguf : read / write sample models * gguf : add comments * refactor : reduce code duplication and better API (#2415) * gguf : expose the gguf_type enum through the API for now * gguf : add array support * gguf.py : some code style changes * convert.py : start a new simplified implementation by removing old stuff * convert.py : remove GGML vocab + other obsolete stuff * GGUF : write tensor (#2426) * WIP: Write tensor * GGUF : Support writing tensors in Python * refactor : rm unused import and upd todos * fix : fix errors upd writing example * rm example.gguf * gitignore *.gguf * undo formatting * gguf : add gguf_find_key (#2438) * gguf.cpp : find key example * ggml.h : add gguf_find_key * ggml.c : add gguf_find_key * gguf : fix writing tensors * gguf : do not hardcode tensor names to read * gguf : write sample tensors to read * gguf : add tokenization constants * quick and dirty conversion example * gguf : fix writing gguf arrays * gguf : write tensors one by one and code reuse * gguf : fix writing gguf arrays * gguf : write tensors one by one * gguf : write tensors one by one * gguf : write tokenizer data * gguf : upd gguf conversion script * Update convert-llama-h5-to-gguf.py * gguf : handle already encoded string * ggml.h : get array str and f32 * ggml.c : get arr str and f32 * gguf.py : support any type * Update convert-llama-h5-to-gguf.py * gguf : fix set is not subscriptable * gguf : update convert-llama-h5-to-gguf.py * constants.py : add layer norm eps * gguf.py : add layer norm eps and merges * ggml.h : increase GGML_MAX_NAME to 64 * ggml.c : add gguf_get_arr_n * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Makefile : add gptneox gguf example * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Update convert-llama-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * gguf : support custom alignment value * gguf : fix typo in function call * gguf : mmap tensor data example * fix : update convert-llama-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * convert-gptneox-h5-to-gguf.py : Special tokens * gptneox-main.cpp : special tokens * Update gptneox-main.cpp * constants.py : special tokens * gguf.py : accumulate kv and tensor info data + special tokens * convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens * gguf : gguf counterpart of llama-util.h * gguf-util.h : update note * convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens * convert-llama-h5-to-gguf.py : special tokens * Delete gptneox-common.cpp * Delete gptneox-common.h * convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer * gptneox-main.cpp : gpt2 bpe tokenizer * gpt2 bpe tokenizer (handles merges and unicode) * Makefile : remove gptneox-common * gguf.py : bytesarray for gpt2bpe tokenizer * cmpnct_gpt2bpe.hpp : comments * gguf.py : use custom alignment if present * gguf : minor stuff * Update gptneox-main.cpp * map tensor names * convert-gptneox-h5-to-gguf.py : map tensor names * convert-llama-h5-to-gguf.py : map tensor names * gptneox-main.cpp : map tensor names * gguf : start implementing libllama in GGUF (WIP) * gguf : start implementing libllama in GGUF (WIP) * rm binary commited by mistake * upd .gitignore * gguf : calculate n_mult * gguf : inference with 7B model working (WIP) * gguf : rm deprecated function * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : add gguf_get_kv_type * gguf : add gguf_get_kv_type * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver * gguf : rm references to old file formats * gguf : shorter name for member variable * gguf : rm redundant method * gguf : get rid of n_mult, read n_ff from file * Update gguf_tensor_map.py * Update gptneox-main.cpp * gguf : rm references to old file magics * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : quantization is working * gguf : roper closing of file * gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : no need to convert tensors twice * convert-llama-h5-to-gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : simplify nbytes * convert-llama-h5-to-gguf.py : simplify nbytes * gptneox-main.cpp : n_layer --> n_block * constants.py : n_layer --> n_block * gguf.py : n_layer --> n_block * convert-gptneox-h5-to-gguf.py : n_layer --> n_block * convert-llama-h5-to-gguf.py : n_layer --> n_block * gptneox-main.cpp : n_layer --> n_block * Update gguf_tensor_map.py * convert-gptneox-h5-to-gguf.py : load model in parts to save memory * convert-llama-h5-to-gguf.py : load model in parts to save memory * convert : write more metadata for LLaMA * convert : rm quantization version * convert-gptneox-h5-to-gguf.py : add file_type key * gptneox-main.cpp : add file_type key * fix conflicts * gguf : add todos and comments * convert-gptneox-h5-to-gguf.py : tensor name map changes * Create gguf_namemap.py : tensor name map changes * Delete gguf_tensor_map.py * gptneox-main.cpp : tensor name map changes * convert-llama-h5-to-gguf.py : fixes * gguf.py : dont add empty strings * simple : minor style changes * gguf : use UNIX line ending * Create convert-llama-7b-pth-to-gguf.py * llama : sync gguf-llama.cpp with latest llama.cpp (#2608) * llama : sync gguf-llama.cpp with latest llama.cpp * minor : indentation + assert * llama : refactor gguf_buffer and gguf_ctx_buffer * llama : minor * gitignore : add gptneox-main * llama : tokenizer fixes (#2549) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * convert : update convert-new.py with tokenizer fixes (#2614) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * llama : sync gguf-llama with llama (#2613) * llama : sync gguf-llama with llama * tests : fix build + warnings (test-tokenizer-1 still fails) * tests : fix wstring_convert * convert : fix layer names * llama : sync gguf-llama.cpp * convert : update HF converter to new tokenizer voodoo magics * llama : update tokenizer style * convert-llama-h5-to-gguf.py : add token types * constants.py : add token types * gguf.py : add token types * convert-llama-7b-pth-to-gguf.py : add token types * gguf-llama.cpp : fix n_head_kv * convert-llama-h5-to-gguf.py : add 70b gqa support * gguf.py : add tensor data layout * convert-llama-h5-to-gguf.py : add tensor data layout * convert-llama-7b-pth-to-gguf.py : add tensor data layout * gptneox-main.cpp : add tensor data layout * convert-llama-h5-to-gguf.py : clarify the reverse permute * llama : refactor model loading code (#2620) * llama : style formatting + remove helper methods * llama : fix quantization using gguf tool * llama : simplify gguf_file_saver * llama : fix method names * llama : simplify write_header() * llama : no need to pass full file loader to the file saver just gguf_ctx * llama : gguf_file_saver write I32 * llama : refactor tensor names (#2622) * gguf: update tensor names searched in quantization * gguf : define tensor names as constants * gguf : initial write API (not tested yet) * gguf : write to file API (not tested) * gguf : initial write API ready + example * gguf : fix header write * gguf : fixes + simplify example + add ggml_nbytes_pad() * gguf : minor * llama : replace gguf_file_saver with new gguf write API * gguf : streaming support when writing files * gguf : remove oboslete write methods * gguf : remove obosolete gguf_get_arr_xxx API * llama : simplify gguf_file_loader * llama : move hparams and vocab from gguf_file_loader to llama_model_loader * llama : merge gguf-util.h in llama.cpp * llama : reorder definitions in .cpp to match .h * llama : minor simplifications * llama : refactor llama_model_loader (WIP) wip : remove ggml_ctx from llama_model_loader wip : merge gguf_file_loader in llama_model_loader * llama : fix shape prints * llama : fix Windows build + fix norm_rms_eps key * llama : throw error on missing KV paris in model meta data * llama : improve printing + log meta data * llama : switch print order of meta data --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> * gguf : deduplicate (#2629) * gguf : better type names * dedup : CPU + Metal is working * ggml : fix warnings about unused results * llama.cpp : fix line feed and compiler warning * llama : fix strncpy warning + note token_to_str does not write null * llama : restore the original load/save session implementation Will migrate this to GGUF in the future * convert-llama-h5-to-gguf.py : support alt ctx param name * ggml : assert when using ggml_mul with non-F32 src1 * examples : dedup simple --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> * gguf.py : merge all files in gguf.py * convert-new.py : pick #2427 for HF 70B support * examples/gguf : no need to keep q option for quantization any more * llama.cpp : print actual model size * llama.cpp : use ggml_elements() * convert-new.py : output gguf (#2635) * convert-new.py : output gguf (WIP) * convert-new.py : add gguf key-value pairs * llama : add hparams.ctx_train + no longer print ftype * convert-new.py : minor fixes * convert-new.py : vocab-only option should work now * llama : fix tokenizer to use llama_char_to_byte * tests : add new ggml-vocab-llama.gguf * convert-new.py : tensor name mapping * convert-new.py : add map for skipping tensor serialization * convert-new.py : convert script now works * gguf.py : pick some of the refactoring from #2644 * convert-new.py : minor fixes * convert.py : update to support GGUF output * Revert "ci : disable CI temporary to not waste energy" This reverts commit 7e82d25f40386540c2c15226300ad998ecd871ea. * convert.py : n_head_kv optional and .gguf file extension * convert.py : better always have n_head_kv and default it to n_head * llama : sync with recent PRs on master * editorconfig : ignore models folder ggml-ci * ci : update ".bin" to ".gguf" extension ggml-ci * llama : fix llama_model_loader memory leak * gptneox : move as a WIP example * llama : fix lambda capture ggml-ci * ggml : fix bug in gguf_set_kv ggml-ci * common.h : .bin --> .gguf * quantize-stats.cpp : .bin --> .gguf * convert.py : fix HF tensor permuting / unpacking ggml-ci * llama.cpp : typo * llama : throw error if gguf fails to init from file ggml-ci * llama : fix tensor name grepping during quantization ggml-ci * gguf.py : write tensors in a single pass (#2644) * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : style fixes in simple conversion script * gguf : refactor gptneox conversion script * gguf : rename h5 to hf (for HuggingFace) * gguf : refactor pth to gguf conversion script * gguf : rm file_type key and method * gguf.py : fix vertical alignment * gguf.py : indentation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * convert-gptneox-hf-to-gguf.py : fixes * gguf.py : gptneox mapping * convert-llama-hf-to-gguf.py : fixes * convert-llama-7b-pth-to-gguf.py : fixes * ggml.h : reverse GGUF_MAGIC * gguf.py : reverse GGUF_MAGIC * test-tokenizer-0.cpp : fix warning * llama.cpp : print kv general.name * llama.cpp : get special token kv and linefeed token id * llama : print number of tensors per type + print arch + style * tests : update vocab file with new magic * editorconfig : fix whitespaces * llama : re-order functions * llama : remove C++ API + reorganize common source in /common dir * llama : minor API updates * llama : avoid hardcoded special tokens * llama : fix MPI build ggml-ci * llama : introduce enum llama_vocab_type + remove hardcoded string constants * convert-falcon-hf-to-gguf.py : falcon HF --> gguf conversion, not tested * falcon-main.cpp : falcon inference example * convert-falcon-hf-to-gguf.py : remove extra kv * convert-gptneox-hf-to-gguf.py : remove extra kv * convert-llama-7b-pth-to-gguf.py : remove extra kv * convert-llama-hf-to-gguf.py : remove extra kv * gguf.py : fix for falcon 40b * falcon-main.cpp : fix for falcon 40b * convert-falcon-hf-to-gguf.py : update ref * convert-falcon-hf-to-gguf.py : add tensor data layout * cmpnct_gpt2bpe.hpp : fixes * falcon-main.cpp : fixes * gptneox-main.cpp : fixes * cmpnct_gpt2bpe.hpp : remove non-general stuff * Update examples/server/README.md Co-authored-by: slaren <slarengh@gmail.com> * cmpnct_gpt2bpe.hpp : cleanup * convert-llama-hf-to-gguf.py : special tokens * convert-llama-7b-pth-to-gguf.py : special tokens * convert-permute-debug.py : permute debug print * convert-permute-debug-master.py : permute debug for master * convert-permute-debug.py : change permute type of attn_q * convert.py : 70b model working (change attn_q permute) * Delete convert-permute-debug-master.py * Delete convert-permute-debug.py * convert-llama-hf-to-gguf.py : fix attn_q permute * gguf.py : fix rope scale kv * convert-llama-hf-to-gguf.py : rope scale and added tokens * convert-llama-7b-pth-to-gguf.py : rope scale and added tokens * llama.cpp : use rope scale kv * convert-llama-7b-pth-to-gguf.py : rope scale fix * convert-llama-hf-to-gguf.py : rope scale fix * py : fix whitespace * gguf : add Python script to convert GGMLv3 LLaMA models to GGUF (#2682) * First pass at converting GGMLv3 LLaMA models to GGUF * Cleanups, better output during conversion * Fix vocab space conversion logic * More vocab conversion fixes * Add description to converted GGUF files * Improve help text, expand warning * Allow specifying name and description for output GGUF * Allow overriding vocab and hyperparams from original model metadata * Use correct params override var name * Fix wrong type size for Q8_K Better handling of original style metadata * Set default value for gguf add_tensor raw_shape KW arg * llama : improve token type support (#2668) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * llama : add API for token type ggml-ci * tests : use new tokenizer type API (#2692) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * Improve commentary * Use token type API in test-tokenizer-1.cpp * py : cosmetics * readme : add notice about new file format ggml-ci --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> Co-authored-by: goerch <jhr.walter@t-online.de> Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
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### Hot topics
readme : update hot topics
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readme : update hot topics
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- Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631
- Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590
readme : update hot topics
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- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
readme : incoming BREAKING CHANGE
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----
Add Misc section + update hot topics + minor fixes
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readme : add TOC and Pygmalion instructions (#1359)
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<details>
<summary>Table of Contents</summary>
<ol>
<li>
<a href="#description">Description</a>
</li>
<li>
<a href="#usage">Usage</a>
<ul>
<li><a href="#get-the-code">Get the Code</a></li>
<li><a href="#build">Build</a></li>
<li><a href="#blas-build">BLAS Build</a></li>
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
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<li><a href="#prepare-and-quantize">Prepare and Quantize</a></li>
<li><a href="#run-the-quantized-model">Run the quantized model</a></li>
readme : add TOC and Pygmalion instructions (#1359)
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<li><a href="#memorydisk-requirements">Memory/Disk Requirements</a></li>
<li><a href="#quantization">Quantization</a></li>
<li><a href="#interactive-mode">Interactive mode</a></li>
docs : add grammar docs (#2701) * docs : add grammar docs * tweaks to grammar guide * rework GBNF example to be a commented grammar
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<li><a href="#constrained-output-with-grammars">Constrained output with grammars</a></li>
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
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<li><a href="#instruct-mode">Instruct mode</a></li>
<li><a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a></li>
readme : add TOC and Pygmalion instructions (#1359)
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<li><a href="#seminal-papers-and-background-on-the-models">Seminal papers and background on the models</a></li>
<li><a href="#perplexity-measuring-model-quality">Perplexity (measuring model quality)</a></li>
<li><a href="#android">Android</a></li>
<li><a href="#docker">Docker</a></li>
</ul>
</li>
<li><a href="#contributing">Contributing</a></li>
<li><a href="#coding-guidelines">Coding guidelines</a></li>
<li><a href="#docs">Docs</a></li>
</ol>
</details>
Create README.md
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## Description
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
variety of hardware - locally and in the cloud.
Create README.md
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README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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- Plain C/C++ implementation without any dependencies
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
readme: add missing info (#1324)
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- AVX, AVX2 and AVX512 support for x86 architectures
readme : update (#5572) Added 1.5-bit on README.md
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- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP)
- Vulkan, SYCL, and (partial) OpenCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
Create README.md
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README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has
improved significantly thanks to many contributions. It is the main playground for developing new features for the
[ggml](https://github.com/ggerganov/ggml) library.
Update README.md
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readme : change logo + add bindings + add uis + add wiki
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**Supported platforms:**
README: add "Supported platforms" + update hot topics
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- [X] Mac OS
- [X] Linux
Update contribution section, hot topics, limitations, etc.
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- [X] Windows (via CMake)
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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- [X] Docker
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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- [X] FreeBSD
README: add "Supported platforms" + update hot topics
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readme : change logo + add bindings + add uis + add wiki
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**Supported models:**
readme : add GPT4All instructions (close #588)
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readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
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Typically finetunes of the base models below are supported as well.
readme : update supported models
2023-03-30 19:31:54 +00:00
- [X] LLaMA 🦙
Obtaining LLaMA 2 instructions (#2308) * Obtaining LLaMA 2 instructions * Removed sharing warning for LLaMA 2 * Linked TheBloke's GGML repos * Add LLaMA 2 to list of supported models * Added LLaMA 2 usage instructions * Added links to LLaMA 2 70B models
2023-07-28 01:14:11 +00:00
- [x] LLaMA 2 🦙🦙
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-05 14:55:10 +00:00
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
readme : update hot topics
2023-08-23 20:41:16 +00:00
- [X] Falcon
readme : Add Chinese LLaMA-2 / Alpaca-2 to supported models (#2475) * add support for chinese llama-2 / alpaca-2 * remove white spaces
2023-08-02 06:18:31 +00:00
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
readme : update supported models
2023-03-30 19:31:54 +00:00
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
Add BAIR's Koala to supported models (#877)
2023-04-10 20:41:53 +00:00
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
readme : update hot-topics & models, detail windows release in usage (#3615) * Update README.md * Update README.md * Update README.md * move "Running on Windows" section below "Prepare data and run" --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-10-17 18:13:21 +00:00
- [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
readme : update hot topics + model links (#3399)
2023-09-29 12:50:35 +00:00
- [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187)
readme : update models, cuda + ppl instructions (#3510)
2023-10-06 19:13:36 +00:00
- [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
readme : update hot-topics & models, detail windows release in usage (#3615) * Update README.md * Update README.md * Update README.md * move "Running on Windows" section below "Prepare data and run" --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-10-17 18:13:21 +00:00
- [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410)
Add MPT model to supported models in README.md (#3574)
2023-10-10 23:02:49 +00:00
- [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417)
readme : update hot-topics & models, detail windows release in usage (#3615) * Update README.md * Update README.md * Update README.md * move "Running on Windows" section below "Prepare data and run" --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-10-17 18:13:21 +00:00
- [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553)
readme : update supported model list (#4457)
2023-12-14 07:38:49 +00:00
- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
readme : add phi, orion 14b, internlm2, and yi-VL to readme (#5362)
2024-02-06 14:06:48 +00:00
- [X] [StableLM models](https://huggingface.co/stabilityai)
readme : update supported model list (#4457)
2023-12-14 07:38:49 +00:00
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
llama : add PLaMo model (#3557) * add plamo mock * add tensor loading * plamo convert * update norm * able to compile * fix norm_rms_eps hparam * runnable * use inp_pos * seems ok * update kqv code * remove develop code * update README * shuffle attn_q.weight and attn_output.weight for broadcasting * remove plamo_llm_build_kqv and use llm_build_kqv * fix style * update * llama : remove obsolete KQ_scale * plamo : fix tensor names for correct GPU offload --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-24 13:35:49 +00:00
- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)
readme : add phi, orion 14b, internlm2, and yi-VL to readme (#5362)
2024-02-06 14:06:48 +00:00
- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
gpt2 : Add gpt2 architecture integration (#4555)
2023-12-28 14:03:57 +00:00
- [x] [GPT-2](https://huggingface.co/gpt2)
readme : add phi, orion 14b, internlm2, and yi-VL to readme (#5362)
2024-02-06 14:06:48 +00:00
- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118)
- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
readme : add CodeShell models to the supported models list (#5330)
2024-02-05 07:41:38 +00:00
- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
llama : add `gemma` model (#5631) There are couple things in this architecture: 1. Shared input and output embedding parameters. 2. Key length and value length are not derived from `n_embd`. More information about the models can be found at https://ai.google.dev/gemma. GGUFs can be downloaded from https://huggingface.co/google.
2024-02-21 13:08:22 +00:00
- [x] [Gemma](https://ai.google.dev/gemma)
readme : update supported model list (#4457)
2023-12-14 07:38:49 +00:00
**Multimodal models:**
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-05 14:55:10 +00:00
- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e)
- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
readme : update supported model list (#4457)
2023-12-14 07:38:49 +00:00
- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
readme : add MobileVLM 1.7B/3B to the supported models list (#5107) Co-authored-by: Chenxiaotao03 <chenxiaotao03@meituan.com>
2024-01-25 20:14:32 +00:00
- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
readme : add phi, orion 14b, internlm2, and yi-VL to readme (#5362)
2024-02-06 14:06:48 +00:00
- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
readme : update hot-topics & models, detail windows release in usage (#3615) * Update README.md * Update README.md * Update README.md * move "Running on Windows" section below "Prepare data and run" --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-10-17 18:13:21 +00:00
readme : change logo + add bindings + add uis + add wiki
2023-04-05 15:56:20 +00:00
**Bindings:**
- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
readme : remove unsupported node.js library (#3703) - https://github.com/Atome-FE/llama-node is quite out of date - doesn't support recent/current llama.cpp functionality
2023-10-22 18:16:43 +00:00
- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
readme : add lgrammel/modelfusion JS/TS client for llama.cpp (#4814)
2024-01-07 20:24:11 +00:00
- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
readme : add JavaScript/Wasm repo (#5415)
2024-02-09 10:17:00 +00:00
- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
readme : add Ruby bindings (#1029)
2023-04-17 19:34:35 +00:00
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
readme : add link to rust bindings (#5148) * added link to another set of rust bindings with brief note on differences. * fixed link name
2024-01-28 08:30:44 +00:00
- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
readme : add C#/.NET bindings repo (#1409)
2023-05-12 05:39:40 +00:00
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
readme : add Scala 3 bindings repo (#2010)
2023-06-26 19:47:59 +00:00
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
Add link to clojure bindings to Readme. (#2659)
2023-08-18 19:39:22 +00:00
- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
readme : add react-native binding (#2869)
2023-08-29 09:30:10 +00:00
- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
docs : add java-llama.cpp to README.md (#2935)
2023-09-01 13:36:14 +00:00
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
readme : add zig bindings (#4581)
2023-12-22 06:49:54 +00:00
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
Add a dart/flutter binding to README.md (#4882)
2024-01-20 08:05:43 +00:00
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
readme : change logo + add bindings + add uis + add wiki
2023-04-05 15:56:20 +00:00
**UI:**
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-05 14:55:10 +00:00
Unless otherwise noted these projects are open-source with permissive licensing:
- [iohub/collama](https://github.com/iohub/coLLaMA)
- [janhq/jan](https://github.com/janhq/jan) (AGPL)
readme : change logo + add bindings + add uis + add wiki
2023-04-05 15:56:20 +00:00
- [nat/openplayground](https://github.com/nat/openplayground)
readme : update ui list (#5354)
2024-02-07 06:16:48 +00:00
- [Faraday](https://faraday.dev/) (proprietary)
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-05 14:55:10 +00:00
- [LMStudio](https://lmstudio.ai/) (proprietary)
readme : add LocalAI to the availables UI (#5629)
2024-02-21 14:39:10 +00:00
- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-05 14:55:10 +00:00
- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile)
- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all)
- [ollama/ollama](https://github.com/ollama/ollama)
- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
readme : add FreeChat (#4248)
2023-11-29 07:16:34 +00:00
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
Add Ava in the list of llama.cpp UIs (#4362)
2024-02-07 18:44:52 +00:00
- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
Adding Emeltal reference to UI list (#4629)
2023-12-25 16:09:53 +00:00
- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)
README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-02-05 14:55:10 +00:00
- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
- [semperai/amica](https://github.com/semperai/amica)
- [withcatai/catai](https://github.com/withcatai/catai)
readme : update UI list (#5605) * Add maid to ui list * Specify licence
2024-02-20 10:00:23 +00:00
- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
readme : add GPT4All instructions (close #588)
2023-03-29 16:37:20 +00:00
Update README.md
2023-03-11 10:31:21 +00:00
---
Create README.md
2023-03-10 19:47:46 +00:00
readme : update hot topics
2023-08-23 20:41:16 +00:00
Here is a typical run using LLaMA v2 13B on M2 Ultra:
Create README.md
2023-03-10 19:47:46 +00:00
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
```
readme : update hot topics
2023-08-23 20:41:16 +00:00
$ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
minor : fix trailing whitespace
2023-08-23 20:43:00 +00:00
I llama.cpp build info:
Create README.md
2023-03-10 19:47:46 +00:00
I UNAME_S: Darwin
I UNAME_P: arm
I UNAME_M: arm64
readme : update hot topics
2023-08-23 20:41:16 +00:00
I CFLAGS: -I. -O3 -std=c11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE
I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS
Create README.md
2023-03-10 19:47:46 +00:00
I LDFLAGS: -framework Accelerate
readme : update hot topics
2023-08-23 20:41:16 +00:00
I CC: Apple clang version 14.0.3 (clang-1403.0.22.14.1)
I CXX: Apple clang version 14.0.3 (clang-1403.0.22.14.1)
Create README.md
2023-03-10 19:47:46 +00:00
Update README.md
2023-03-10 22:09:19 +00:00
make: Nothing to be done for `default'.
readme : update hot topics
2023-08-23 20:41:16 +00:00
main: build = 1041 (cf658ad)
main: seed = 1692823051
llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest))
llama_model_loader: - type f32: 81 tensors
llama_model_loader: - type q4_0: 281 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_print_meta: format = GGUF V1 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 4096
llm_load_print_meta: n_ctx = 512
llm_load_print_meta: n_embd = 5120
llm_load_print_meta: n_head = 40
llm_load_print_meta: n_head_kv = 40
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: f_norm_eps = 1.0e-05
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: n_ff = 13824
llm_load_print_meta: freq_base = 10000.0
llm_load_print_meta: freq_scale = 1
llm_load_print_meta: model type = 13B
llm_load_print_meta: model ftype = mostly Q4_0
llm_load_print_meta: model size = 13.02 B
llm_load_print_meta: general.name = LLaMA v2
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.11 MB
llm_load_tensors: mem required = 7024.01 MB (+ 400.00 MB per state)
...................................................................................................
llama_new_context_with_model: kv self size = 400.00 MB
llama_new_context_with_model: compute buffer total size = 75.41 MB
minor : fix trailing whitespace
2023-08-23 20:43:00 +00:00
system_info: n_threads = 16 / 24 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 |
readme : update hot topics
2023-08-23 20:41:16 +00:00
sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 0
Building a website can be done in 10 simple steps:
Step 1: Find the right website platform.
Step 2: Choose your domain name and hosting plan.
Step 3: Design your website layout.
Step 4: Write your website content and add images.
Step 5: Install security features to protect your site from hackers or spammers
Step 6: Test your website on multiple browsers, mobile devices, operating systems etc…
Step 7: Test it again with people who are not related to you personally – friends or family members will work just fine!
Step 8: Start marketing and promoting the website via social media channels or paid ads
Step 9: Analyze how many visitors have come to your site so far, what type of people visit more often than others (e.g., men vs women) etc…
Step 10: Continue to improve upon all aspects mentioned above by following trends in web design and staying up-to-date on new technologies that can enhance user experience even further!
How does a Website Work?
A website works by having pages, which are made of HTML code. This code tells your computer how to display the content on each page you visit – whether it’s an image or text file (like PDFs). In order for someone else’s browser not only be able but also want those same results when accessing any given URL; some additional steps need taken by way of programming scripts that will add functionality such as making links clickable!
The most common type is called static HTML pages because they remain unchanged over time unless modified manually (either through editing files directly or using an interface such as WordPress). They are usually served up via HTTP protocols – this means anyone can access them without having any special privileges like being part of a group who is allowed into restricted areas online; however, there may still exist some limitations depending upon where one lives geographically speaking.
How to
llama_print_timings: load time = 576.45 ms
llama_print_timings: sample time = 283.10 ms / 400 runs ( 0.71 ms per token, 1412.91 tokens per second)
llama_print_timings: prompt eval time = 599.83 ms / 19 tokens ( 31.57 ms per token, 31.68 tokens per second)
llama_print_timings: eval time = 24513.59 ms / 399 runs ( 61.44 ms per token, 16.28 tokens per second)
llama_print_timings: total time = 25431.49 ms
Create README.md
2023-03-10 19:47:46 +00:00
```
Update README.md
2023-03-10 22:51:46 +00:00
And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook:
https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8b4f-add84093ffff.mp4
Create README.md
2023-03-10 19:47:46 +00:00
## Usage
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
Here are the end-to-end binary build and model conversion steps for most supported models.
zig : update build.zig (#872) * update * update readme * minimize the changes. --------- Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
2023-04-13 13:43:22 +00:00
### Get the Code
Support all LLaMA models + change Q4_0 quantization storage
2023-03-11 08:47:09 +00:00
Create README.md
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```bash
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
zig : update build.zig (#872) * update * update readme * minimize the changes. --------- Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
2023-04-13 13:43:22 +00:00
```
### Build
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
In order to build llama.cpp you have three different options.
zig : update build.zig (#872) * update * update readme * minimize the changes. --------- Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
2023-04-13 13:43:22 +00:00
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
- Using `make`:
- On Linux or MacOS:
zig : update build.zig (#872) * update * update readme * minimize the changes. --------- Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
2023-04-13 13:43:22 +00:00
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
```bash
make
```
- On Windows:
Correcting link to w64devkit (#1214) Correcting link to w64devkit (change seeto to skeeto).
2023-04-28 14:22:48 +00:00
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
2. Extract `w64devkit` on your pc.
3. Run `w64devkit.exe`.
4. Use the `cd` command to reach the `llama.cpp` folder.
5. From here you can run:
```bash
make
```
Create README.md
2023-03-10 19:47:46 +00:00
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
- Using `CMake`:
readme : update with CMake and windows example (#748) * README: Update with CMake and windows example * README: update with code-review for cmake build
2023-04-05 14:36:12 +00:00
zig : update build.zig (#872) * update * update readme * minimize the changes. --------- Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
2023-04-13 13:43:22 +00:00
```bash
mkdir build
cd build
cmake ..
cmake --build . --config Release
```
[Zig] Fixing Zig build and improvements (#2554) * Fix zig after console.o was split * Better include and flag management * Change LTO to option
2023-08-17 20:11:18 +00:00
- Using `Zig` (version 0.11 or later):
Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C,
it's also possible to cross compile for other operating systems and architectures:
zig : update build.zig (#872) * update * update readme * minimize the changes. --------- Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
2023-04-13 13:43:22 +00:00
```bash
[Zig] Fixing Zig build and improvements (#2554) * Fix zig after console.o was split * Better include and flag management * Change LTO to option
2023-08-17 20:11:18 +00:00
zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c
zig : update build.zig (#872) * update * update readme * minimize the changes. --------- Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
2023-04-13 13:43:22 +00:00
```
[Zig] Fixing Zig build and improvements (#2554) * Fix zig after console.o was split * Better include and flag management * Change LTO to option
2023-08-17 20:11:18 +00:00
The `zig targets` command will give you valid options to use.
make : fix CLBLAST compile support in FreeBSD (#2331) * Fix Makefile for CLBLAST compile support and instructions for compile llama.cpp FreeBSD * More general use-case for CLBLAST support (Linux and FreeBSD)
2023-07-23 11:52:08 +00:00
- Using `gmake` (FreeBSD):
1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics)
2. Add your user to **video** group
3. Install compilation dependencies.
```bash
sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \
opencl clblast openblas
readme : minor (#5204) This is about tuning the code formatting of the README file
2024-01-30 09:16:38 +00:00
gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4
make : fix CLBLAST compile support in FreeBSD (#2331) * Fix Makefile for CLBLAST compile support and instructions for compile llama.cpp FreeBSD * More general use-case for CLBLAST support (Linux and FreeBSD)
2023-07-23 11:52:08 +00:00
```
**Notes:** With this packages you can build llama.cpp with OPENBLAS and
CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read
the instructions for use and activate this options in this document below.
llama : Metal inference (#1642) * mtl : export the LLaMA computation graph * ci : disable temporary * mtl : adapt the MNIST example as starter * mtl : no need for mtl-export tool, add cli arg for main instead * mtl : export just a small part of the graph for now to make it easier * mtl : move MSL code into separate file for easy editing * mtl : initial get_rows_q4_0 kernel * mtl : confirmed get_rows_q4_0 is working correctly * mtl : add rms_norm kernel + confirm working * mtl : add mul kernel + confirm working * mtl : initial mul_mat Q4 kernel (wrong results) * mtl : mul_mat fixes (still wrong) * mtl : another mul_mat Q4 (still does not work) * mtl : working mul_mat q4 * ggml : fix handling of "view" ops in ggml_graph_import() * mtl : add rope kernel * mtl : add reshape and transpose handling * ggml : store offset as opt arg for ggml_view_xd() operators * mtl : add cpy kernel + handle view ops * mtl : confirm f16 x f32 attention mul mat * mtl : add scale kernel * mtl : add diag_mask_inf kernel * mtl : fix soft_max kernel * ggml : update ggml_nbytes() to handle non-contiguous tensors * mtl : verify V tensor contents * mtl : add f32 -> f32 cpy kernel * mtl : add silu kernel * mtl : add non-broadcast mul kernel * mtl : full GPU inference of the computation graph * mtl : optimize rms_norm and soft_max kernels * mtl : add f16 mat x f32 vec multiplication kernel * mtl : fix bug in f16 x f32 mul mat + speed-up computation * mtl : faster mul_mat_q4_0_f32 kernel * mtl : fix kernel signature + roll inner loop * mtl : more threads for rms_norm + better timing * mtl : remove printfs from inner loop * mtl : simplify implementation * mtl : add save/load vocab to ggml file * mtl : plug Metal inference into llama.cpp (very quick-n-dirty) * mtl : make it work with main example Lots of hacks but at least now it generates text * mtl : preparing for merge * mtl : clean-up ggml mtl interface + suport scratch / inplace * mtl : remove temp / debug code * metal : final refactoring and simplification * Revert "ci : disable temporary" This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63. * metal : add comments * metal : clean-up stuff, fix typos * readme : add Metal instructions * readme : add example for main
2023-06-04 20:34:30 +00:00
### Metal Build
build : on Mac OS enable Metal by default (#2901) * build : on Mac OS enable Metal by default * make : try to fix build on Linux * make : move targets back to the top * make : fix target clean * llama : enable GPU inference by default with Metal * llama : fix vocab_only logic when GPU is enabled * common : better `n_gpu_layers` assignment * readme : update Metal instructions * make : fix merge conflict remnants * gitignore : metal
2023-09-04 19:26:24 +00:00
On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or the `LLAMA_METAL=OFF` cmake option.
llama : Metal inference (#1642) * mtl : export the LLaMA computation graph * ci : disable temporary * mtl : adapt the MNIST example as starter * mtl : no need for mtl-export tool, add cli arg for main instead * mtl : export just a small part of the graph for now to make it easier * mtl : move MSL code into separate file for easy editing * mtl : initial get_rows_q4_0 kernel * mtl : confirmed get_rows_q4_0 is working correctly * mtl : add rms_norm kernel + confirm working * mtl : add mul kernel + confirm working * mtl : initial mul_mat Q4 kernel (wrong results) * mtl : mul_mat fixes (still wrong) * mtl : another mul_mat Q4 (still does not work) * mtl : working mul_mat q4 * ggml : fix handling of "view" ops in ggml_graph_import() * mtl : add rope kernel * mtl : add reshape and transpose handling * ggml : store offset as opt arg for ggml_view_xd() operators * mtl : add cpy kernel + handle view ops * mtl : confirm f16 x f32 attention mul mat * mtl : add scale kernel * mtl : add diag_mask_inf kernel * mtl : fix soft_max kernel * ggml : update ggml_nbytes() to handle non-contiguous tensors * mtl : verify V tensor contents * mtl : add f32 -> f32 cpy kernel * mtl : add silu kernel * mtl : add non-broadcast mul kernel * mtl : full GPU inference of the computation graph * mtl : optimize rms_norm and soft_max kernels * mtl : add f16 mat x f32 vec multiplication kernel * mtl : fix bug in f16 x f32 mul mat + speed-up computation * mtl : faster mul_mat_q4_0_f32 kernel * mtl : fix kernel signature + roll inner loop * mtl : more threads for rms_norm + better timing * mtl : remove printfs from inner loop * mtl : simplify implementation * mtl : add save/load vocab to ggml file * mtl : plug Metal inference into llama.cpp (very quick-n-dirty) * mtl : make it work with main example Lots of hacks but at least now it generates text * mtl : preparing for merge * mtl : clean-up ggml mtl interface + suport scratch / inplace * mtl : remove temp / debug code * metal : final refactoring and simplification * Revert "ci : disable temporary" This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63. * metal : add comments * metal : clean-up stuff, fix typos * readme : add Metal instructions * readme : add example for main
2023-06-04 20:34:30 +00:00
typo : it is `--n-gpu-layers` not `--gpu-layers` (#3592) fixed a typo in the MacOS Metal run doco
2023-10-12 11:10:50 +00:00
When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line
build : on Mac OS enable Metal by default (#2901) * build : on Mac OS enable Metal by default * make : try to fix build on Linux * make : move targets back to the top * make : fix target clean * llama : enable GPU inference by default with Metal * llama : fix vocab_only logic when GPU is enabled * common : better `n_gpu_layers` assignment * readme : update Metal instructions * make : fix merge conflict remnants * gitignore : metal
2023-09-04 19:26:24 +00:00
argument.
llama : Metal inference (#1642) * mtl : export the LLaMA computation graph * ci : disable temporary * mtl : adapt the MNIST example as starter * mtl : no need for mtl-export tool, add cli arg for main instead * mtl : export just a small part of the graph for now to make it easier * mtl : move MSL code into separate file for easy editing * mtl : initial get_rows_q4_0 kernel * mtl : confirmed get_rows_q4_0 is working correctly * mtl : add rms_norm kernel + confirm working * mtl : add mul kernel + confirm working * mtl : initial mul_mat Q4 kernel (wrong results) * mtl : mul_mat fixes (still wrong) * mtl : another mul_mat Q4 (still does not work) * mtl : working mul_mat q4 * ggml : fix handling of "view" ops in ggml_graph_import() * mtl : add rope kernel * mtl : add reshape and transpose handling * ggml : store offset as opt arg for ggml_view_xd() operators * mtl : add cpy kernel + handle view ops * mtl : confirm f16 x f32 attention mul mat * mtl : add scale kernel * mtl : add diag_mask_inf kernel * mtl : fix soft_max kernel * ggml : update ggml_nbytes() to handle non-contiguous tensors * mtl : verify V tensor contents * mtl : add f32 -> f32 cpy kernel * mtl : add silu kernel * mtl : add non-broadcast mul kernel * mtl : full GPU inference of the computation graph * mtl : optimize rms_norm and soft_max kernels * mtl : add f16 mat x f32 vec multiplication kernel * mtl : fix bug in f16 x f32 mul mat + speed-up computation * mtl : faster mul_mat_q4_0_f32 kernel * mtl : fix kernel signature + roll inner loop * mtl : more threads for rms_norm + better timing * mtl : remove printfs from inner loop * mtl : simplify implementation * mtl : add save/load vocab to ggml file * mtl : plug Metal inference into llama.cpp (very quick-n-dirty) * mtl : make it work with main example Lots of hacks but at least now it generates text * mtl : preparing for merge * mtl : clean-up ggml mtl interface + suport scratch / inplace * mtl : remove temp / debug code * metal : final refactoring and simplification * Revert "ci : disable temporary" This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63. * metal : add comments * metal : clean-up stuff, fix typos * readme : add Metal instructions * readme : add example for main
2023-06-04 20:34:30 +00:00
mpi : add support for distributed inference via MPI (#2099) * MPI support, first cut * fix warnings, update README * fixes * wrap includes * PR comments * Update CMakeLists.txt * Add GH workflow, fix test * Add info to README * mpi : trying to move more MPI stuff into ggml-mpi (WIP) (#2099) * mpi : add names for layer inputs + prep ggml_mpi_graph_compute() * mpi : move all MPI logic into ggml-mpi Not tested yet * mpi : various fixes - communication now works but results are wrong * mpi : fix output tensor after MPI compute (still not working) * mpi : fix inference * mpi : minor * Add OpenMPI to GH action * [mpi] continue-on-error: true * mpi : fix after master merge * [mpi] Link MPI C++ libraries to fix OpenMPI * tests : fix new llama_backend API * [mpi] use MPI_INT32_T * mpi : factor out recv / send in functions and reuse * mpi : extend API to allow usage with outer backends (e.g. Metal) --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-10 15:49:56 +00:00
### MPI Build
MPI lets you distribute the computation over a cluster of machines. Because of the serial nature of LLM prediction, this won't yield any end-to-end speed-ups, but it will let you run larger models than would otherwise fit into RAM on a single machine.
First you will need MPI libraries installed on your system. The two most popular (only?) options are [MPICH](https://www.mpich.org) and [OpenMPI](https://www.open-mpi.org). Either can be installed with a package manager (`apt`, Homebrew, MacPorts, etc).
Next you will need to build the project with `LLAMA_MPI` set to true on all machines; if you're building with `make`, you will also need to specify an MPI-capable compiler (when building with CMake, this is configured automatically):
- Using `make`:
```bash
make CC=mpicc CXX=mpicxx LLAMA_MPI=1
```
- Using `CMake`:
```bash
cmake -S . -B build -DLLAMA_MPI=ON
```
Once the programs are built, download/convert the weights on all of the machines in your cluster. The paths to the weights and programs should be identical on all machines.
Next, ensure password-less SSH access to each machine from the primary host, and create a `hostfile` with a list of the hostnames and their relative "weights" (slots). If you want to use localhost for computation, use its local subnet IP address rather than the loopback address or "localhost".
Here is an example hostfile:
```
192.168.0.1:2
malvolio.local:1
```
The above will distribute the computation across 2 processes on the first host and 1 process on the second host. Each process will use roughly an equal amount of RAM. Try to keep these numbers small, as inter-process (intra-host) communication is expensive.
Finally, you're ready to run a computation using `mpirun`:
```bash
gguf : new file format with flexible meta data (beta) (#2398) * gguf : first API pass * gguf : read header + meta data * gguf : read tensor info * gguf : initial model loading - not tested * gguf : add gguf_get_tensor_name() * gguf : do not support passing existing ggml_context to gguf_init * gguf : simplify gguf_get_val * gguf : gguf.c is now part of ggml.c * gguf : read / write sample models * gguf : add comments * refactor : reduce code duplication and better API (#2415) * gguf : expose the gguf_type enum through the API for now * gguf : add array support * gguf.py : some code style changes * convert.py : start a new simplified implementation by removing old stuff * convert.py : remove GGML vocab + other obsolete stuff * GGUF : write tensor (#2426) * WIP: Write tensor * GGUF : Support writing tensors in Python * refactor : rm unused import and upd todos * fix : fix errors upd writing example * rm example.gguf * gitignore *.gguf * undo formatting * gguf : add gguf_find_key (#2438) * gguf.cpp : find key example * ggml.h : add gguf_find_key * ggml.c : add gguf_find_key * gguf : fix writing tensors * gguf : do not hardcode tensor names to read * gguf : write sample tensors to read * gguf : add tokenization constants * quick and dirty conversion example * gguf : fix writing gguf arrays * gguf : write tensors one by one and code reuse * gguf : fix writing gguf arrays * gguf : write tensors one by one * gguf : write tensors one by one * gguf : write tokenizer data * gguf : upd gguf conversion script * Update convert-llama-h5-to-gguf.py * gguf : handle already encoded string * ggml.h : get array str and f32 * ggml.c : get arr str and f32 * gguf.py : support any type * Update convert-llama-h5-to-gguf.py * gguf : fix set is not subscriptable * gguf : update convert-llama-h5-to-gguf.py * constants.py : add layer norm eps * gguf.py : add layer norm eps and merges * ggml.h : increase GGML_MAX_NAME to 64 * ggml.c : add gguf_get_arr_n * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Makefile : add gptneox gguf example * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Update convert-llama-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * gguf : support custom alignment value * gguf : fix typo in function call * gguf : mmap tensor data example * fix : update convert-llama-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * convert-gptneox-h5-to-gguf.py : Special tokens * gptneox-main.cpp : special tokens * Update gptneox-main.cpp * constants.py : special tokens * gguf.py : accumulate kv and tensor info data + special tokens * convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens * gguf : gguf counterpart of llama-util.h * gguf-util.h : update note * convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens * convert-llama-h5-to-gguf.py : special tokens * Delete gptneox-common.cpp * Delete gptneox-common.h * convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer * gptneox-main.cpp : gpt2 bpe tokenizer * gpt2 bpe tokenizer (handles merges and unicode) * Makefile : remove gptneox-common * gguf.py : bytesarray for gpt2bpe tokenizer * cmpnct_gpt2bpe.hpp : comments * gguf.py : use custom alignment if present * gguf : minor stuff * Update gptneox-main.cpp * map tensor names * convert-gptneox-h5-to-gguf.py : map tensor names * convert-llama-h5-to-gguf.py : map tensor names * gptneox-main.cpp : map tensor names * gguf : start implementing libllama in GGUF (WIP) * gguf : start implementing libllama in GGUF (WIP) * rm binary commited by mistake * upd .gitignore * gguf : calculate n_mult * gguf : inference with 7B model working (WIP) * gguf : rm deprecated function * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : add gguf_get_kv_type * gguf : add gguf_get_kv_type * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver * gguf : rm references to old file formats * gguf : shorter name for member variable * gguf : rm redundant method * gguf : get rid of n_mult, read n_ff from file * Update gguf_tensor_map.py * Update gptneox-main.cpp * gguf : rm references to old file magics * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : quantization is working * gguf : roper closing of file * gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : no need to convert tensors twice * convert-llama-h5-to-gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : simplify nbytes * convert-llama-h5-to-gguf.py : simplify nbytes * gptneox-main.cpp : n_layer --> n_block * constants.py : n_layer --> n_block * gguf.py : n_layer --> n_block * convert-gptneox-h5-to-gguf.py : n_layer --> n_block * convert-llama-h5-to-gguf.py : n_layer --> n_block * gptneox-main.cpp : n_layer --> n_block * Update gguf_tensor_map.py * convert-gptneox-h5-to-gguf.py : load model in parts to save memory * convert-llama-h5-to-gguf.py : load model in parts to save memory * convert : write more metadata for LLaMA * convert : rm quantization version * convert-gptneox-h5-to-gguf.py : add file_type key * gptneox-main.cpp : add file_type key * fix conflicts * gguf : add todos and comments * convert-gptneox-h5-to-gguf.py : tensor name map changes * Create gguf_namemap.py : tensor name map changes * Delete gguf_tensor_map.py * gptneox-main.cpp : tensor name map changes * convert-llama-h5-to-gguf.py : fixes * gguf.py : dont add empty strings * simple : minor style changes * gguf : use UNIX line ending * Create convert-llama-7b-pth-to-gguf.py * llama : sync gguf-llama.cpp with latest llama.cpp (#2608) * llama : sync gguf-llama.cpp with latest llama.cpp * minor : indentation + assert * llama : refactor gguf_buffer and gguf_ctx_buffer * llama : minor * gitignore : add gptneox-main * llama : tokenizer fixes (#2549) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * convert : update convert-new.py with tokenizer fixes (#2614) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * llama : sync gguf-llama with llama (#2613) * llama : sync gguf-llama with llama * tests : fix build + warnings (test-tokenizer-1 still fails) * tests : fix wstring_convert * convert : fix layer names * llama : sync gguf-llama.cpp * convert : update HF converter to new tokenizer voodoo magics * llama : update tokenizer style * convert-llama-h5-to-gguf.py : add token types * constants.py : add token types * gguf.py : add token types * convert-llama-7b-pth-to-gguf.py : add token types * gguf-llama.cpp : fix n_head_kv * convert-llama-h5-to-gguf.py : add 70b gqa support * gguf.py : add tensor data layout * convert-llama-h5-to-gguf.py : add tensor data layout * convert-llama-7b-pth-to-gguf.py : add tensor data layout * gptneox-main.cpp : add tensor data layout * convert-llama-h5-to-gguf.py : clarify the reverse permute * llama : refactor model loading code (#2620) * llama : style formatting + remove helper methods * llama : fix quantization using gguf tool * llama : simplify gguf_file_saver * llama : fix method names * llama : simplify write_header() * llama : no need to pass full file loader to the file saver just gguf_ctx * llama : gguf_file_saver write I32 * llama : refactor tensor names (#2622) * gguf: update tensor names searched in quantization * gguf : define tensor names as constants * gguf : initial write API (not tested yet) * gguf : write to file API (not tested) * gguf : initial write API ready + example * gguf : fix header write * gguf : fixes + simplify example + add ggml_nbytes_pad() * gguf : minor * llama : replace gguf_file_saver with new gguf write API * gguf : streaming support when writing files * gguf : remove oboslete write methods * gguf : remove obosolete gguf_get_arr_xxx API * llama : simplify gguf_file_loader * llama : move hparams and vocab from gguf_file_loader to llama_model_loader * llama : merge gguf-util.h in llama.cpp * llama : reorder definitions in .cpp to match .h * llama : minor simplifications * llama : refactor llama_model_loader (WIP) wip : remove ggml_ctx from llama_model_loader wip : merge gguf_file_loader in llama_model_loader * llama : fix shape prints * llama : fix Windows build + fix norm_rms_eps key * llama : throw error on missing KV paris in model meta data * llama : improve printing + log meta data * llama : switch print order of meta data --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> * gguf : deduplicate (#2629) * gguf : better type names * dedup : CPU + Metal is working * ggml : fix warnings about unused results * llama.cpp : fix line feed and compiler warning * llama : fix strncpy warning + note token_to_str does not write null * llama : restore the original load/save session implementation Will migrate this to GGUF in the future * convert-llama-h5-to-gguf.py : support alt ctx param name * ggml : assert when using ggml_mul with non-F32 src1 * examples : dedup simple --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> * gguf.py : merge all files in gguf.py * convert-new.py : pick #2427 for HF 70B support * examples/gguf : no need to keep q option for quantization any more * llama.cpp : print actual model size * llama.cpp : use ggml_elements() * convert-new.py : output gguf (#2635) * convert-new.py : output gguf (WIP) * convert-new.py : add gguf key-value pairs * llama : add hparams.ctx_train + no longer print ftype * convert-new.py : minor fixes * convert-new.py : vocab-only option should work now * llama : fix tokenizer to use llama_char_to_byte * tests : add new ggml-vocab-llama.gguf * convert-new.py : tensor name mapping * convert-new.py : add map for skipping tensor serialization * convert-new.py : convert script now works * gguf.py : pick some of the refactoring from #2644 * convert-new.py : minor fixes * convert.py : update to support GGUF output * Revert "ci : disable CI temporary to not waste energy" This reverts commit 7e82d25f40386540c2c15226300ad998ecd871ea. * convert.py : n_head_kv optional and .gguf file extension * convert.py : better always have n_head_kv and default it to n_head * llama : sync with recent PRs on master * editorconfig : ignore models folder ggml-ci * ci : update ".bin" to ".gguf" extension ggml-ci * llama : fix llama_model_loader memory leak * gptneox : move as a WIP example * llama : fix lambda capture ggml-ci * ggml : fix bug in gguf_set_kv ggml-ci * common.h : .bin --> .gguf * quantize-stats.cpp : .bin --> .gguf * convert.py : fix HF tensor permuting / unpacking ggml-ci * llama.cpp : typo * llama : throw error if gguf fails to init from file ggml-ci * llama : fix tensor name grepping during quantization ggml-ci * gguf.py : write tensors in a single pass (#2644) * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : style fixes in simple conversion script * gguf : refactor gptneox conversion script * gguf : rename h5 to hf (for HuggingFace) * gguf : refactor pth to gguf conversion script * gguf : rm file_type key and method * gguf.py : fix vertical alignment * gguf.py : indentation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * convert-gptneox-hf-to-gguf.py : fixes * gguf.py : gptneox mapping * convert-llama-hf-to-gguf.py : fixes * convert-llama-7b-pth-to-gguf.py : fixes * ggml.h : reverse GGUF_MAGIC * gguf.py : reverse GGUF_MAGIC * test-tokenizer-0.cpp : fix warning * llama.cpp : print kv general.name * llama.cpp : get special token kv and linefeed token id * llama : print number of tensors per type + print arch + style * tests : update vocab file with new magic * editorconfig : fix whitespaces * llama : re-order functions * llama : remove C++ API + reorganize common source in /common dir * llama : minor API updates * llama : avoid hardcoded special tokens * llama : fix MPI build ggml-ci * llama : introduce enum llama_vocab_type + remove hardcoded string constants * convert-falcon-hf-to-gguf.py : falcon HF --> gguf conversion, not tested * falcon-main.cpp : falcon inference example * convert-falcon-hf-to-gguf.py : remove extra kv * convert-gptneox-hf-to-gguf.py : remove extra kv * convert-llama-7b-pth-to-gguf.py : remove extra kv * convert-llama-hf-to-gguf.py : remove extra kv * gguf.py : fix for falcon 40b * falcon-main.cpp : fix for falcon 40b * convert-falcon-hf-to-gguf.py : update ref * convert-falcon-hf-to-gguf.py : add tensor data layout * cmpnct_gpt2bpe.hpp : fixes * falcon-main.cpp : fixes * gptneox-main.cpp : fixes * cmpnct_gpt2bpe.hpp : remove non-general stuff * Update examples/server/README.md Co-authored-by: slaren <slarengh@gmail.com> * cmpnct_gpt2bpe.hpp : cleanup * convert-llama-hf-to-gguf.py : special tokens * convert-llama-7b-pth-to-gguf.py : special tokens * convert-permute-debug.py : permute debug print * convert-permute-debug-master.py : permute debug for master * convert-permute-debug.py : change permute type of attn_q * convert.py : 70b model working (change attn_q permute) * Delete convert-permute-debug-master.py * Delete convert-permute-debug.py * convert-llama-hf-to-gguf.py : fix attn_q permute * gguf.py : fix rope scale kv * convert-llama-hf-to-gguf.py : rope scale and added tokens * convert-llama-7b-pth-to-gguf.py : rope scale and added tokens * llama.cpp : use rope scale kv * convert-llama-7b-pth-to-gguf.py : rope scale fix * convert-llama-hf-to-gguf.py : rope scale fix * py : fix whitespace * gguf : add Python script to convert GGMLv3 LLaMA models to GGUF (#2682) * First pass at converting GGMLv3 LLaMA models to GGUF * Cleanups, better output during conversion * Fix vocab space conversion logic * More vocab conversion fixes * Add description to converted GGUF files * Improve help text, expand warning * Allow specifying name and description for output GGUF * Allow overriding vocab and hyperparams from original model metadata * Use correct params override var name * Fix wrong type size for Q8_K Better handling of original style metadata * Set default value for gguf add_tensor raw_shape KW arg * llama : improve token type support (#2668) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * llama : add API for token type ggml-ci * tests : use new tokenizer type API (#2692) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * Improve commentary * Use token type API in test-tokenizer-1.cpp * py : cosmetics * readme : add notice about new file format ggml-ci --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> Co-authored-by: goerch <jhr.walter@t-online.de> Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
2023-08-21 20:07:43 +00:00
mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
mpi : add support for distributed inference via MPI (#2099) * MPI support, first cut * fix warnings, update README * fixes * wrap includes * PR comments * Update CMakeLists.txt * Add GH workflow, fix test * Add info to README * mpi : trying to move more MPI stuff into ggml-mpi (WIP) (#2099) * mpi : add names for layer inputs + prep ggml_mpi_graph_compute() * mpi : move all MPI logic into ggml-mpi Not tested yet * mpi : various fixes - communication now works but results are wrong * mpi : fix output tensor after MPI compute (still not working) * mpi : fix inference * mpi : minor * Add OpenMPI to GH action * [mpi] continue-on-error: true * mpi : fix after master merge * [mpi] Link MPI C++ libraries to fix OpenMPI * tests : fix new llama_backend API * [mpi] use MPI_INT32_T * mpi : factor out recv / send in functions and reuse * mpi : extend API to allow usage with outer backends (e.g. Metal) --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-10 15:49:56 +00:00
```
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
### BLAS Build
readme : fix (#4135) * fix: readme * chore: resolve comments * chore: resolve comments
2023-11-30 21:49:21 +00:00
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
docs : add performance troubleshoot + example benchmark documentation (#1674) * test anchor link * test table * add benchmarks * Add performance troubleshoot & benchmark * add benchmarks * remove unneeded line --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 20:32:36 +00:00
- #### Accelerate Framework:
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
docs : add performance troubleshoot + example benchmark documentation (#1674) * test anchor link * test table * add benchmarks * Add performance troubleshoot & benchmark * add benchmarks * remove unneeded line --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 20:32:36 +00:00
- #### OpenBLAS:
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
- Using `make`:
- On Linux:
```bash
make LLAMA_OPENBLAS=1
```
- On Windows:
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
2. Download the latest version of [OpenBLAS for Windows](https://github.com/xianyi/OpenBLAS/releases).
3. Extract `w64devkit` on your pc.
4. From the OpenBLAS zip that you just downloaded copy `libopenblas.a`, located inside the `lib` folder, inside `w64devkit\x86_64-w64-mingw32\lib`.
5. From the same OpenBLAS zip copy the content of the `include` folder inside `w64devkit\x86_64-w64-mingw32\include`.
6. Run `w64devkit.exe`.
7. Use the `cd` command to reach the `llama.cpp` folder.
8. From here you can run:
```bash
make LLAMA_OPENBLAS=1
```
- Using `CMake` on Linux:
```bash
mkdir build
cd build
feature : support blis and other blas implementation (#1536) * feature: add blis support * feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927 * fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake * Fix typo in INTEGER Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix: blas changes on ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-20 14:58:31 +00:00
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
cmake --build . --config Release
```
docs : add performance troubleshoot + example benchmark documentation (#1674) * test anchor link * test table * add benchmarks * Add performance troubleshoot & benchmark * add benchmarks * remove unneeded line --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 20:32:36 +00:00
- #### BLIS
feature : support blis and other blas implementation (#1536) * feature: add blis support * feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927 * fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake * Fix typo in INTEGER Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix: blas changes on ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-20 14:58:31 +00:00
doc : fix wrong address of BLIS.md (#1772) Signed-off-by: Aisuko <urakiny@gmail.com>
2023-06-10 14:08:11 +00:00
Check [BLIS.md](docs/BLIS.md) for more information.
feature : support blis and other blas implementation (#1536) * feature: add blis support * feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927 * fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake * Fix typo in INTEGER Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix: blas changes on ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-20 14:58:31 +00:00
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
- #### SYCL
SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md).
ggml : add ggml_cpu_has_avx_vnni() (#4589) * feat: add avx_vnni based on intel documents * ggml: add avx vnni based on intel document * llama: add avx vnni information display * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * Update ggml.c Fix indentation upgate Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-30 08:07:48 +00:00
- #### Intel oneMKL
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md).
ggml : add ggml_cpu_has_avx_vnni() (#4589) * feat: add avx_vnni based on intel documents * ggml: add avx vnni based on intel document * llama: add avx vnni information display * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * Update ggml.c Fix indentation upgate Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-30 08:07:48 +00:00
- Using manual oneAPI installation:
By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
```bash
mkdir build
cd build
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation
ggml : add ggml_cpu_has_avx_vnni() (#4589) * feat: add avx_vnni based on intel documents * ggml: add avx vnni based on intel document * llama: add avx vnni information display * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * Update ggml.c Fix indentation upgate Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-30 08:07:48 +00:00
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
cmake --build . --config Release
```
feature : support blis and other blas implementation (#1536) * feature: add blis support * feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927 * fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake * Fix typo in INTEGER Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix: blas changes on ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-20 14:58:31 +00:00
ggml : add ggml_cpu_has_avx_vnni() (#4589) * feat: add avx_vnni based on intel documents * ggml: add avx vnni based on intel document * llama: add avx vnni information display * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * Update ggml.c Fix indentation upgate Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-30 08:07:48 +00:00
- Using oneAPI docker image:
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
ggml : add ggml_cpu_has_avx_vnni() (#4589) * feat: add avx_vnni based on intel documents * ggml: add avx vnni based on intel document * llama: add avx vnni information display * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * docs: add more details about using oneMKL and oneAPI for intel processors * Update ggml.c Fix indentation upgate Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-30 08:07:48 +00:00
Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
feature : support blis and other blas implementation (#1536) * feature: add blis support * feature: allow all BLA_VENDOR to be assigned in cmake arguments. align with whisper.cpp pr 927 * fix: version detection for BLA_SIZEOF_INTEGER, recover min version of cmake * Fix typo in INTEGER Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Fix: blas changes on ci --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-05-20 14:58:31 +00:00
docs : add performance troubleshoot + example benchmark documentation (#1674) * test anchor link * test table * add benchmarks * Add performance troubleshoot & benchmark * add benchmarks * remove unneeded line --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 20:32:36 +00:00
- #### cuBLAS
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
readme : update models, cuda + ppl instructions (#3510)
2023-10-06 19:13:36 +00:00
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).
cuda : fix jetson compile error (#4560) * fix old jetson compile error * Update Makefile * update jetson detect and cuda version detect * update cuda marco define * update makefile and cuda,fix some issue * Update README.md Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update Makefile * Update README.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-12-22 15:11:12 +00:00
For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.
Updating build instructions to include BLAS support (#1183) * Updated build information First update to the build instructions to include BLAS. * Update README.md * Update information about BLAS * Better BLAS explanation Adding a clearer BLAS explanation and adding a link to download the CUDA toolkit. * Better BLAS explanation * BLAS for Mac Specifying that BLAS is already supported on Macs using the Accelerate Framework. * Clarify the effect of BLAS * Windows Make instructions Added the instructions to build with Make on Windows * Fixing typo * Fix trailing whitespace
2023-04-26 20:03:03 +00:00
- Using `make`:
```bash
make LLAMA_CUBLAS=1
```
- Using `CMake`:
```bash
mkdir build
cd build
cmake .. -DLLAMA_CUBLAS=ON
cmake --build . --config Release
```
Convert vector to f16 for dequantize mul mat vec (#1913) * Convert vector to f16 for dmmv * compile option * Added compilation option description to README * Changed cmake CUDA_ARCHITECTURES from "OFF" to "native"
2023-06-19 08:23:56 +00:00
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
CUDA: mmq CLI option, fixed mmq build issues (#2453)
2023-07-31 13:44:35 +00:00
<!---
| LLAMA_CUDA_CUBLAS | Boolean | false | Use cuBLAS instead of custom CUDA kernels for prompt processing. Faster for all quantization formats except for q4_0 and q8_0, especially for k-quants. Increases VRAM usage (700 MiB for 7b, 970 MiB for 13b, 1430 MiB for 33b). |
--->
CUDA: enable peer access between devices (#2470)
2023-09-17 14:37:53 +00:00
| Option | Legal values | Default | Description |
|--------------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. |
| LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
| LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
Add info about CUDA_VISIBLE_DEVICES (#1682)
2023-06-03 13:35:20 +00:00
ROCm Port (#1087) * use hipblas based on cublas * Update Makefile for the Cuda kernels * Expand arch list and make it overrideable * Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5) * add hipBLAS to README * new build arg LLAMA_CUDA_MMQ_Y * fix half2 decomposition * Add intrinsics polyfills for AMD * AMD assembly optimized __dp4a * Allow overriding CC_TURING * use "ROCm" instead of "CUDA" * ignore all build dirs * Add Dockerfiles * fix llama-bench * fix -nommq help for non CUDA/HIP --------- Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com> Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com> Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com> Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Co-authored-by: jammm <2500920+jammm@users.noreply.github.com> Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
2023-08-25 09:09:42 +00:00
- #### hipBLAS
readme : fix typo (#3043) * readme : fix typo acceleation -> acceleration * Update README.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-09-08 16:04:32 +00:00
This provides BLAS acceleration on HIP-supported AMD GPUs.
ROCm Port (#1087) * use hipblas based on cublas * Update Makefile for the Cuda kernels * Expand arch list and make it overrideable * Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5) * add hipBLAS to README * new build arg LLAMA_CUDA_MMQ_Y * fix half2 decomposition * Add intrinsics polyfills for AMD * AMD assembly optimized __dp4a * Allow overriding CC_TURING * use "ROCm" instead of "CUDA" * ignore all build dirs * Add Dockerfiles * fix llama-bench * fix -nommq help for non CUDA/HIP --------- Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com> Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com> Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com> Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Co-authored-by: jammm <2500920+jammm@users.noreply.github.com> Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
2023-08-25 09:09:42 +00:00
Make sure to have ROCm installed.
You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html).
- Using `make`:
```bash
make LLAMA_HIPBLAS=1
```
cuda : ROCm AMD Unified Memory Architecture (UMA) handling (#4449) * AMD ROCm: handle UMA memory VRAM expansions This resolves #2797 by allowing ROCm AMD GPU users with a UMA to dynamically expand the VRAM allocated to the GPU. Without this, AMD ROCm users with shared CPU/GPU memory usually are stuck with the BIOS-set (or fixed) framebuffer VRAM, making it impossible to load more than 1-2 layers. Note that the model is duplicated in RAM because it's loaded once for the CPU and then copied into a second set of allocations that are managed by the HIP UMA system. We can fix this later. * clarify build process for ROCm on linux with cmake * avoid using deprecated ROCm hipMallocHost * keep simplifying the change required for UMA * cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON
2023-12-21 19:45:32 +00:00
- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
ROCm Port (#1087) * use hipblas based on cublas * Update Makefile for the Cuda kernels * Expand arch list and make it overrideable * Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5) * add hipBLAS to README * new build arg LLAMA_CUDA_MMQ_Y * fix half2 decomposition * Add intrinsics polyfills for AMD * AMD assembly optimized __dp4a * Allow overriding CC_TURING * use "ROCm" instead of "CUDA" * ignore all build dirs * Add Dockerfiles * fix llama-bench * fix -nommq help for non CUDA/HIP --------- Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com> Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com> Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com> Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Co-authored-by: jammm <2500920+jammm@users.noreply.github.com> Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
2023-08-25 09:09:42 +00:00
```bash
cuda : ROCm AMD Unified Memory Architecture (UMA) handling (#4449) * AMD ROCm: handle UMA memory VRAM expansions This resolves #2797 by allowing ROCm AMD GPU users with a UMA to dynamically expand the VRAM allocated to the GPU. Without this, AMD ROCm users with shared CPU/GPU memory usually are stuck with the BIOS-set (or fixed) framebuffer VRAM, making it impossible to load more than 1-2 layers. Note that the model is duplicated in RAM because it's loaded once for the CPU and then copied into a second set of allocations that are managed by the HIP UMA system. We can fix this later. * clarify build process for ROCm on linux with cmake * avoid using deprecated ROCm hipMallocHost * keep simplifying the change required for UMA * cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON
2023-12-21 19:45:32 +00:00
CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \
cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
&& cmake --build build -- -j 16
ROCm Port (#1087) * use hipblas based on cublas * Update Makefile for the Cuda kernels * Expand arch list and make it overrideable * Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5) * add hipBLAS to README * new build arg LLAMA_CUDA_MMQ_Y * fix half2 decomposition * Add intrinsics polyfills for AMD * AMD assembly optimized __dp4a * Allow overriding CC_TURING * use "ROCm" instead of "CUDA" * ignore all build dirs * Add Dockerfiles * fix llama-bench * fix -nommq help for non CUDA/HIP --------- Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com> Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com> Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com> Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Co-authored-by: jammm <2500920+jammm@users.noreply.github.com> Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
2023-08-25 09:09:42 +00:00
```
cuda : ROCm AMD Unified Memory Architecture (UMA) handling (#4449) * AMD ROCm: handle UMA memory VRAM expansions This resolves #2797 by allowing ROCm AMD GPU users with a UMA to dynamically expand the VRAM allocated to the GPU. Without this, AMD ROCm users with shared CPU/GPU memory usually are stuck with the BIOS-set (or fixed) framebuffer VRAM, making it impossible to load more than 1-2 layers. Note that the model is duplicated in RAM because it's loaded once for the CPU and then copied into a second set of allocations that are managed by the HIP UMA system. We can fix this later. * clarify build process for ROCm on linux with cmake * avoid using deprecated ROCm hipMallocHost * keep simplifying the change required for UMA * cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON
2023-12-21 19:45:32 +00:00
On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`.
make : add LLAMA_HIP_UMA option (#4587) NB: LLAMA_HIP_UMA=1 (or any value) adds MK_CPPFLAG -DGGML_HIP_UMA
2023-12-22 08:03:25 +00:00
However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
- Using `make` (example for target gfx1030, build with 16 CPU threads):
```bash
make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gxf1030
```
cuda : ROCm AMD Unified Memory Architecture (UMA) handling (#4449) * AMD ROCm: handle UMA memory VRAM expansions This resolves #2797 by allowing ROCm AMD GPU users with a UMA to dynamically expand the VRAM allocated to the GPU. Without this, AMD ROCm users with shared CPU/GPU memory usually are stuck with the BIOS-set (or fixed) framebuffer VRAM, making it impossible to load more than 1-2 layers. Note that the model is duplicated in RAM because it's loaded once for the CPU and then copied into a second set of allocations that are managed by the HIP UMA system. We can fix this later. * clarify build process for ROCm on linux with cmake * avoid using deprecated ROCm hipMallocHost * keep simplifying the change required for UMA * cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON
2023-12-21 19:45:32 +00:00
- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
readme : update ROCm Windows instructions (#4122) * Update README.md * Update README.md Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> --------- Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
2023-11-20 15:02:46 +00:00
```bash
readme : use PATH for Windows ROCm (#4195) * Update README.md to use PATH for Windows ROCm * Update README.md * Update README.md
2023-11-24 07:52:39 +00:00
set PATH=%HIP_PATH%\bin;%PATH%
readme : update ROCm Windows instructions (#4122) * Update README.md * Update README.md Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> --------- Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
2023-11-20 15:02:46 +00:00
mkdir build
cd build
cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..
cmake --build .
```
Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
cuda : ROCm AMD Unified Memory Architecture (UMA) handling (#4449) * AMD ROCm: handle UMA memory VRAM expansions This resolves #2797 by allowing ROCm AMD GPU users with a UMA to dynamically expand the VRAM allocated to the GPU. Without this, AMD ROCm users with shared CPU/GPU memory usually are stuck with the BIOS-set (or fixed) framebuffer VRAM, making it impossible to load more than 1-2 layers. Note that the model is duplicated in RAM because it's loaded once for the CPU and then copied into a second set of allocations that are managed by the HIP UMA system. We can fix this later. * clarify build process for ROCm on linux with cmake * avoid using deprecated ROCm hipMallocHost * keep simplifying the change required for UMA * cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON
2023-12-21 19:45:32 +00:00
Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`.
readme : update ROCm Windows instructions (#4122) * Update README.md * Update README.md Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> --------- Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
2023-11-20 15:02:46 +00:00
ROCm Port (#1087) * use hipblas based on cublas * Update Makefile for the Cuda kernels * Expand arch list and make it overrideable * Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5) * add hipBLAS to README * new build arg LLAMA_CUDA_MMQ_Y * fix half2 decomposition * Add intrinsics polyfills for AMD * AMD assembly optimized __dp4a * Allow overriding CC_TURING * use "ROCm" instead of "CUDA" * ignore all build dirs * Add Dockerfiles * fix llama-bench * fix -nommq help for non CUDA/HIP --------- Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com> Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com> Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com> Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Co-authored-by: jammm <2500920+jammm@users.noreply.github.com> Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
2023-08-25 09:09:42 +00:00
The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
cuda : ROCm AMD Unified Memory Architecture (UMA) handling (#4449) * AMD ROCm: handle UMA memory VRAM expansions This resolves #2797 by allowing ROCm AMD GPU users with a UMA to dynamically expand the VRAM allocated to the GPU. Without this, AMD ROCm users with shared CPU/GPU memory usually are stuck with the BIOS-set (or fixed) framebuffer VRAM, making it impossible to load more than 1-2 layers. Note that the model is duplicated in RAM because it's loaded once for the CPU and then copied into a second set of allocations that are managed by the HIP UMA system. We can fix this later. * clarify build process for ROCm on linux with cmake * avoid using deprecated ROCm hipMallocHost * keep simplifying the change required for UMA * cmake: enable UMA-compatible allocation when LLAMA_HIP_UMA=ON
2023-12-21 19:45:32 +00:00
If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
ROCm Port (#1087) * use hipblas based on cublas * Update Makefile for the Cuda kernels * Expand arch list and make it overrideable * Fix multi GPU on multiple amd architectures with rocblas_initialize() (#5) * add hipBLAS to README * new build arg LLAMA_CUDA_MMQ_Y * fix half2 decomposition * Add intrinsics polyfills for AMD * AMD assembly optimized __dp4a * Allow overriding CC_TURING * use "ROCm" instead of "CUDA" * ignore all build dirs * Add Dockerfiles * fix llama-bench * fix -nommq help for non CUDA/HIP --------- Co-authored-by: YellowRoseCx <80486540+YellowRoseCx@users.noreply.github.com> Co-authored-by: ardfork <134447697+ardfork@users.noreply.github.com> Co-authored-by: funnbot <22226942+funnbot@users.noreply.github.com> Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Co-authored-by: jammm <2500920+jammm@users.noreply.github.com> Co-authored-by: jdecourval <7315817+jdecourval@users.noreply.github.com>
2023-08-25 09:09:42 +00:00
The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
docs : add performance troubleshoot + example benchmark documentation (#1674) * test anchor link * test table * add benchmarks * Add performance troubleshoot & benchmark * add benchmarks * remove unneeded line --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 20:32:36 +00:00
- #### CLBlast
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU.
You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK).
- For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed.
readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace
2023-09-02 12:53:18 +00:00
- For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page.
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
- <details>
<summary>Installing the OpenCL SDK from source</summary>
```sh
git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
mkdir OpenCL-SDK/build
cd OpenCL-SDK/build
cmake .. -DBUILD_DOCS=OFF \
-DBUILD_EXAMPLES=OFF \
-DBUILD_TESTING=OFF \
-DOPENCL_SDK_BUILD_SAMPLES=OFF \
-DOPENCL_SDK_TEST_SAMPLES=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path
```
</details>
readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace
2023-09-02 12:53:18 +00:00
##### Installing CLBlast
Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages.
Alternatively, they may be built from source.
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
- <details>
readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace
2023-09-02 12:53:18 +00:00
<summary>Windows:</summary>
```cmd
set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast\build
cd CLBlast\build
cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/CLBlast
```
- <details>
<summary>Unix:</summary>
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
```sh
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast/build
Fix typo (#1949)
2023-06-20 12:42:40 +00:00
cd CLBlast/build
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path
```
Add info about CUDA_VISIBLE_DEVICES (#1682)
2023-06-03 13:35:20 +00:00
Where `/some/path` is where the built library will be installed (default is `/usr/local`).
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
</details>
readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace
2023-09-02 12:53:18 +00:00
##### Building Llama with CLBlast
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
- Build with make:
```sh
make LLAMA_CLBLAST=1
```
readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace
2023-09-02 12:53:18 +00:00
- CMake (Unix):
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
```sh
mkdir build
cd build
docs: Fix typo CLBlast_DIR var. (#3330)
2023-09-25 18:24:52 +00:00
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
cmake --build . --config Release
```
readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace
2023-09-02 12:53:18 +00:00
- CMake (Windows):
```cmd
set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
cmake --build . --config Release
cmake --install . --prefix C:/LlamaCPP
```
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace
2023-09-02 12:53:18 +00:00
##### Running Llama with CLBlast
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
llama : Metal inference (#1642) * mtl : export the LLaMA computation graph * ci : disable temporary * mtl : adapt the MNIST example as starter * mtl : no need for mtl-export tool, add cli arg for main instead * mtl : export just a small part of the graph for now to make it easier * mtl : move MSL code into separate file for easy editing * mtl : initial get_rows_q4_0 kernel * mtl : confirmed get_rows_q4_0 is working correctly * mtl : add rms_norm kernel + confirm working * mtl : add mul kernel + confirm working * mtl : initial mul_mat Q4 kernel (wrong results) * mtl : mul_mat fixes (still wrong) * mtl : another mul_mat Q4 (still does not work) * mtl : working mul_mat q4 * ggml : fix handling of "view" ops in ggml_graph_import() * mtl : add rope kernel * mtl : add reshape and transpose handling * ggml : store offset as opt arg for ggml_view_xd() operators * mtl : add cpy kernel + handle view ops * mtl : confirm f16 x f32 attention mul mat * mtl : add scale kernel * mtl : add diag_mask_inf kernel * mtl : fix soft_max kernel * ggml : update ggml_nbytes() to handle non-contiguous tensors * mtl : verify V tensor contents * mtl : add f32 -> f32 cpy kernel * mtl : add silu kernel * mtl : add non-broadcast mul kernel * mtl : full GPU inference of the computation graph * mtl : optimize rms_norm and soft_max kernels * mtl : add f16 mat x f32 vec multiplication kernel * mtl : fix bug in f16 x f32 mul mat + speed-up computation * mtl : faster mul_mat_q4_0_f32 kernel * mtl : fix kernel signature + roll inner loop * mtl : more threads for rms_norm + better timing * mtl : remove printfs from inner loop * mtl : simplify implementation * mtl : add save/load vocab to ggml file * mtl : plug Metal inference into llama.cpp (very quick-n-dirty) * mtl : make it work with main example Lots of hacks but at least now it generates text * mtl : preparing for merge * mtl : clean-up ggml mtl interface + suport scratch / inplace * mtl : remove temp / debug code * metal : final refactoring and simplification * Revert "ci : disable temporary" This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63. * metal : add comments * metal : clean-up stuff, fix typos * readme : add Metal instructions * readme : add example for main
2023-06-04 20:34:30 +00:00
The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does.
Add documentation about CLBlast (#1604) Installing, compiling and using.
2023-05-27 15:47:55 +00:00
To select the correct platform (driver) and device (GPU), you can use the environment variables `GGML_OPENCL_PLATFORM` and `GGML_OPENCL_DEVICE`.
The selection can be a number (starting from 0) or a text string to search:
```sh
GGML_OPENCL_PLATFORM=1 ./main ...
GGML_OPENCL_DEVICE=2 ./main ...
GGML_OPENCL_PLATFORM=Intel ./main ...
GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ...
```
The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful.
Using the variables it is possible to select a CPU-based driver as well, if so desired.
You can get a list of platforms and devices from the `clinfo -l` command, etc.
Documented CUDA reproducibility, added warning (#1346)
2023-05-08 00:42:01 +00:00
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
- #### Vulkan
ggml : add unified SYCL backend for Intel GPUs (#2690) * first update for migration * update init_cublas * add debug functio, commit all help code * step 1 * step 2 * step3 add fp16, slower 31->28 * add GGML_LIST_DEVICE function * step 5 format device and print * step6, enhance error check, remove CUDA macro, enhance device id to fix none-zero id issue * support main device is non-zero * step7 add debug for code path, rm log * step 8, rename all macro & func from cuda by sycl * fix error of select non-zero device, format device list * ren ggml-sycl.hpp -> ggml-sycl.h * clear CMAKE to rm unused lib and options * correct queue: rm dtct:get_queue * add print tensor function to debug * fix error: wrong result in 658746bb26702e50f2c59c0e4ada8e9da6010481 * summary dpct definition in one header file to replace folder:dpct * refactor device log * mv dpct definition from folder dpct to ggml-sycl.h * update readme, refactor build script * fix build with sycl * set nthread=1 when sycl, increase performance * add run script, comment debug code * add ls-sycl-device tool * add ls-sycl-device, rm unused files * rm rear space * dos2unix * Update README_sycl.md * fix return type * remove sycl version from include path * restore rm code to fix hang issue * add syc and link for sycl readme * rm original sycl code before refactor * fix code err * add know issue for pvc hang issue * enable SYCL_F16 support * align pr4766 * check for sycl blas, better performance * cleanup 1 * remove extra endif * add build&run script, clean CMakefile, update guide by review comments * rename macro to intel hardware * editor config format * format fixes * format fixes * editor format fix * Remove unused headers * skip build sycl tool for other code path * replace tab by space * fix blas matmul function * fix mac build * restore hip dependency * fix conflict * ren as review comments * mv internal function to .cpp file * export funciton print_sycl_devices(), mv class dpct definition to source file * update CI/action for sycl code, fix CI error of repeat/dup * fix action ID format issue * rm unused strategy * enable llama_f16 in ci * fix conflict * fix build break on MacOS, due to CI of MacOS depend on external ggml, instead of internal ggml * fix ci cases for unsupported data type * revert unrelated changed in cuda cmake remove useless nommq fix typo of GGML_USE_CLBLAS_SYCL * revert hip cmake changes * fix indent * add prefix in func name * revert no mmq * rm cpu blas duplicate * fix no_new_line * fix src1->type==F16 bug. * pass batch offset for F16 src1 * fix batch error * fix wrong code * revert sycl checking in test-sampling * pass void as arguments of ggml_backend_sycl_print_sycl_devices * remove extra blank line in test-sampling * revert setting n_threads in sycl * implement std::isinf for icpx with fast math. * Update ci/run.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add copyright and MIT license declare * update the cmd example --------- Co-authored-by: jianyuzh <jianyu.zhang@intel.com> Co-authored-by: luoyu-intel <yu.luo@intel.com> Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-28 15:56:23 +00:00
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
**With docker**:
ggml : add unified SYCL backend for Intel GPUs (#2690) * first update for migration * update init_cublas * add debug functio, commit all help code * step 1 * step 2 * step3 add fp16, slower 31->28 * add GGML_LIST_DEVICE function * step 5 format device and print * step6, enhance error check, remove CUDA macro, enhance device id to fix none-zero id issue * support main device is non-zero * step7 add debug for code path, rm log * step 8, rename all macro & func from cuda by sycl * fix error of select non-zero device, format device list * ren ggml-sycl.hpp -> ggml-sycl.h * clear CMAKE to rm unused lib and options * correct queue: rm dtct:get_queue * add print tensor function to debug * fix error: wrong result in 658746bb26702e50f2c59c0e4ada8e9da6010481 * summary dpct definition in one header file to replace folder:dpct * refactor device log * mv dpct definition from folder dpct to ggml-sycl.h * update readme, refactor build script * fix build with sycl * set nthread=1 when sycl, increase performance * add run script, comment debug code * add ls-sycl-device tool * add ls-sycl-device, rm unused files * rm rear space * dos2unix * Update README_sycl.md * fix return type * remove sycl version from include path * restore rm code to fix hang issue * add syc and link for sycl readme * rm original sycl code before refactor * fix code err * add know issue for pvc hang issue * enable SYCL_F16 support * align pr4766 * check for sycl blas, better performance * cleanup 1 * remove extra endif * add build&run script, clean CMakefile, update guide by review comments * rename macro to intel hardware * editor config format * format fixes * format fixes * editor format fix * Remove unused headers * skip build sycl tool for other code path * replace tab by space * fix blas matmul function * fix mac build * restore hip dependency * fix conflict * ren as review comments * mv internal function to .cpp file * export funciton print_sycl_devices(), mv class dpct definition to source file * update CI/action for sycl code, fix CI error of repeat/dup * fix action ID format issue * rm unused strategy * enable llama_f16 in ci * fix conflict * fix build break on MacOS, due to CI of MacOS depend on external ggml, instead of internal ggml * fix ci cases for unsupported data type * revert unrelated changed in cuda cmake remove useless nommq fix typo of GGML_USE_CLBLAS_SYCL * revert hip cmake changes * fix indent * add prefix in func name * revert no mmq * rm cpu blas duplicate * fix no_new_line * fix src1->type==F16 bug. * pass batch offset for F16 src1 * fix batch error * fix wrong code * revert sycl checking in test-sampling * pass void as arguments of ggml_backend_sycl_print_sycl_devices * remove extra blank line in test-sampling * revert setting n_threads in sycl * implement std::isinf for icpx with fast math. * Update ci/run.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add copyright and MIT license declare * update the cmd example --------- Co-authored-by: jianyuzh <jianyu.zhang@intel.com> Co-authored-by: luoyu-intel <yu.luo@intel.com> Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-28 15:56:23 +00:00
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
You don't need to install Vulkan SDK. It will be installed inside the container.
ggml : add unified SYCL backend for Intel GPUs (#2690) * first update for migration * update init_cublas * add debug functio, commit all help code * step 1 * step 2 * step3 add fp16, slower 31->28 * add GGML_LIST_DEVICE function * step 5 format device and print * step6, enhance error check, remove CUDA macro, enhance device id to fix none-zero id issue * support main device is non-zero * step7 add debug for code path, rm log * step 8, rename all macro & func from cuda by sycl * fix error of select non-zero device, format device list * ren ggml-sycl.hpp -> ggml-sycl.h * clear CMAKE to rm unused lib and options * correct queue: rm dtct:get_queue * add print tensor function to debug * fix error: wrong result in 658746bb26702e50f2c59c0e4ada8e9da6010481 * summary dpct definition in one header file to replace folder:dpct * refactor device log * mv dpct definition from folder dpct to ggml-sycl.h * update readme, refactor build script * fix build with sycl * set nthread=1 when sycl, increase performance * add run script, comment debug code * add ls-sycl-device tool * add ls-sycl-device, rm unused files * rm rear space * dos2unix * Update README_sycl.md * fix return type * remove sycl version from include path * restore rm code to fix hang issue * add syc and link for sycl readme * rm original sycl code before refactor * fix code err * add know issue for pvc hang issue * enable SYCL_F16 support * align pr4766 * check for sycl blas, better performance * cleanup 1 * remove extra endif * add build&run script, clean CMakefile, update guide by review comments * rename macro to intel hardware * editor config format * format fixes * format fixes * editor format fix * Remove unused headers * skip build sycl tool for other code path * replace tab by space * fix blas matmul function * fix mac build * restore hip dependency * fix conflict * ren as review comments * mv internal function to .cpp file * export funciton print_sycl_devices(), mv class dpct definition to source file * update CI/action for sycl code, fix CI error of repeat/dup * fix action ID format issue * rm unused strategy * enable llama_f16 in ci * fix conflict * fix build break on MacOS, due to CI of MacOS depend on external ggml, instead of internal ggml * fix ci cases for unsupported data type * revert unrelated changed in cuda cmake remove useless nommq fix typo of GGML_USE_CLBLAS_SYCL * revert hip cmake changes * fix indent * add prefix in func name * revert no mmq * rm cpu blas duplicate * fix no_new_line * fix src1->type==F16 bug. * pass batch offset for F16 src1 * fix batch error * fix wrong code * revert sycl checking in test-sampling * pass void as arguments of ggml_backend_sycl_print_sycl_devices * remove extra blank line in test-sampling * revert setting n_threads in sycl * implement std::isinf for icpx with fast math. * Update ci/run.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add copyright and MIT license declare * update the cmd example --------- Co-authored-by: jianyuzh <jianyu.zhang@intel.com> Co-authored-by: luoyu-intel <yu.luo@intel.com> Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-28 15:56:23 +00:00
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
```sh
# Build the image
docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile .
# Then, use it:
docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
```
**Without docker**:
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html)
ggml : add unified SYCL backend for Intel GPUs (#2690) * first update for migration * update init_cublas * add debug functio, commit all help code * step 1 * step 2 * step3 add fp16, slower 31->28 * add GGML_LIST_DEVICE function * step 5 format device and print * step6, enhance error check, remove CUDA macro, enhance device id to fix none-zero id issue * support main device is non-zero * step7 add debug for code path, rm log * step 8, rename all macro & func from cuda by sycl * fix error of select non-zero device, format device list * ren ggml-sycl.hpp -> ggml-sycl.h * clear CMAKE to rm unused lib and options * correct queue: rm dtct:get_queue * add print tensor function to debug * fix error: wrong result in 658746bb26702e50f2c59c0e4ada8e9da6010481 * summary dpct definition in one header file to replace folder:dpct * refactor device log * mv dpct definition from folder dpct to ggml-sycl.h * update readme, refactor build script * fix build with sycl * set nthread=1 when sycl, increase performance * add run script, comment debug code * add ls-sycl-device tool * add ls-sycl-device, rm unused files * rm rear space * dos2unix * Update README_sycl.md * fix return type * remove sycl version from include path * restore rm code to fix hang issue * add syc and link for sycl readme * rm original sycl code before refactor * fix code err * add know issue for pvc hang issue * enable SYCL_F16 support * align pr4766 * check for sycl blas, better performance * cleanup 1 * remove extra endif * add build&run script, clean CMakefile, update guide by review comments * rename macro to intel hardware * editor config format * format fixes * format fixes * editor format fix * Remove unused headers * skip build sycl tool for other code path * replace tab by space * fix blas matmul function * fix mac build * restore hip dependency * fix conflict * ren as review comments * mv internal function to .cpp file * export funciton print_sycl_devices(), mv class dpct definition to source file * update CI/action for sycl code, fix CI error of repeat/dup * fix action ID format issue * rm unused strategy * enable llama_f16 in ci * fix conflict * fix build break on MacOS, due to CI of MacOS depend on external ggml, instead of internal ggml * fix ci cases for unsupported data type * revert unrelated changed in cuda cmake remove useless nommq fix typo of GGML_USE_CLBLAS_SYCL * revert hip cmake changes * fix indent * add prefix in func name * revert no mmq * rm cpu blas duplicate * fix no_new_line * fix src1->type==F16 bug. * pass batch offset for F16 src1 * fix batch error * fix wrong code * revert sycl checking in test-sampling * pass void as arguments of ggml_backend_sycl_print_sycl_devices * remove extra blank line in test-sampling * revert setting n_threads in sycl * implement std::isinf for icpx with fast math. * Update ci/run.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add copyright and MIT license declare * update the cmd example --------- Co-authored-by: jianyuzh <jianyu.zhang@intel.com> Co-authored-by: luoyu-intel <yu.luo@intel.com> Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-28 15:56:23 +00:00
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
For example, on Ubuntu 22.04 (jammy), use the command below:
```bash
wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add -
wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list
apt update -y
apt-get install -y vulkan-sdk
# To verify the installation, use the command below:
vulkaninfo
```
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead.
docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL
2024-02-02 07:56:31 +00:00
Then, build llama.cpp using the cmake command below:
```bash
mkdir -p build
cd build
cmake .. -DLLAMA_VULKAN=1
cmake --build . --config Release
# Test the output binary (with "-ngl 33" to offload all layers to GPU)
./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4
# You should see in the output, ggml_vulkan detected your GPU. For example:
# ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
```
ggml : add unified SYCL backend for Intel GPUs (#2690) * first update for migration * update init_cublas * add debug functio, commit all help code * step 1 * step 2 * step3 add fp16, slower 31->28 * add GGML_LIST_DEVICE function * step 5 format device and print * step6, enhance error check, remove CUDA macro, enhance device id to fix none-zero id issue * support main device is non-zero * step7 add debug for code path, rm log * step 8, rename all macro & func from cuda by sycl * fix error of select non-zero device, format device list * ren ggml-sycl.hpp -> ggml-sycl.h * clear CMAKE to rm unused lib and options * correct queue: rm dtct:get_queue * add print tensor function to debug * fix error: wrong result in 658746bb26702e50f2c59c0e4ada8e9da6010481 * summary dpct definition in one header file to replace folder:dpct * refactor device log * mv dpct definition from folder dpct to ggml-sycl.h * update readme, refactor build script * fix build with sycl * set nthread=1 when sycl, increase performance * add run script, comment debug code * add ls-sycl-device tool * add ls-sycl-device, rm unused files * rm rear space * dos2unix * Update README_sycl.md * fix return type * remove sycl version from include path * restore rm code to fix hang issue * add syc and link for sycl readme * rm original sycl code before refactor * fix code err * add know issue for pvc hang issue * enable SYCL_F16 support * align pr4766 * check for sycl blas, better performance * cleanup 1 * remove extra endif * add build&run script, clean CMakefile, update guide by review comments * rename macro to intel hardware * editor config format * format fixes * format fixes * editor format fix * Remove unused headers * skip build sycl tool for other code path * replace tab by space * fix blas matmul function * fix mac build * restore hip dependency * fix conflict * ren as review comments * mv internal function to .cpp file * export funciton print_sycl_devices(), mv class dpct definition to source file * update CI/action for sycl code, fix CI error of repeat/dup * fix action ID format issue * rm unused strategy * enable llama_f16 in ci * fix conflict * fix build break on MacOS, due to CI of MacOS depend on external ggml, instead of internal ggml * fix ci cases for unsupported data type * revert unrelated changed in cuda cmake remove useless nommq fix typo of GGML_USE_CLBLAS_SYCL * revert hip cmake changes * fix indent * add prefix in func name * revert no mmq * rm cpu blas duplicate * fix no_new_line * fix src1->type==F16 bug. * pass batch offset for F16 src1 * fix batch error * fix wrong code * revert sycl checking in test-sampling * pass void as arguments of ggml_backend_sycl_print_sycl_devices * remove extra blank line in test-sampling * revert setting n_threads in sycl * implement std::isinf for icpx with fast math. * Update ci/run.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Update CMakeLists.txt Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add copyright and MIT license declare * update the cmd example --------- Co-authored-by: jianyuzh <jianyu.zhang@intel.com> Co-authored-by: luoyu-intel <yu.luo@intel.com> Co-authored-by: Meng, Hengyu <hengyu.meng@intel.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2024-01-28 15:56:23 +00:00
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
### Prepare and Quantize
To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
zig : update build.zig (#872) * update * update readme * minimize the changes. --------- Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
2023-04-13 13:43:22 +00:00
```bash
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
# obtain the official LLaMA model weights and place them in ./models
Create README.md
2023-03-10 19:47:46 +00:00
ls ./models
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
llama-2-7b tokenizer_checklist.chk tokenizer.model
readme : minor (#5204) This is about tuning the code formatting of the README file
2024-01-30 09:16:38 +00:00
# [Optional] for models using BPE tokenizers
ls ./models
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
<folder containing weights and tokenizer json> vocab.json
# [Optional] for PyTorch .bin models like Mistral-7B
ls ./models
<folder containing weights and tokenizer json>
Create README.md
2023-03-10 19:47:46 +00:00
Include Python dependencies in README (#6)
2023-03-11 05:47:26 +00:00
# install Python dependencies
py : new conversion script (#545) Current status: Working, except for the latest GPTQ-for-LLaMa format that includes `g_idx`. This turns out to require changes to GGML, so for now it only works if you use the `--outtype` option to dequantize it back to f16 (which is pointless except for debugging). I also included some cleanup for the C++ code. This script is meant to replace all the existing conversion scripts (including the ones that convert from older GGML formats), while also adding support for some new formats. Specifically, I've tested with: - [x] `LLaMA` (original) - [x] `llama-65b-4bit` - [x] `alpaca-native` - [x] `alpaca-native-4bit` - [x] LLaMA converted to 'transformers' format using `convert_llama_weights_to_hf.py` - [x] `alpaca-native` quantized with `--true-sequential --act-order --groupsize 128` (dequantized only) - [x] same as above plus `--save_safetensors` - [x] GPT4All - [x] stock unversioned ggml - [x] ggmh There's enough overlap in the logic needed to handle these different cases that it seemed best to move to a single script. I haven't tried this with Alpaca-LoRA because I don't know where to find it. Useful features: - Uses multiple threads for a speedup in some cases (though the Python GIL limits the gain, and sometimes it's disk-bound anyway). - Combines split models into a single file (both the intra-tensor split of the original and the inter-tensor split of 'transformers' format files). Single files are more convenient to work with and more friendly to future changes to use memory mapping on the C++ side. To accomplish this without increasing memory requirements, it has some custom loading code which avoids loading whole input files into memory at once. - Because of the custom loading code, it no longer depends in PyTorch, which might make installing dependencies slightly easier or faster... although it still depends on NumPy and sentencepiece, so I don't know if there's any meaningful difference. In any case, I also added a requirements.txt file to lock the dependency versions in case of any future breaking changes. - Type annotations checked with mypy. - Some attempts to be extra user-friendly: - The script tries to be forgiving with arguments, e.g. you can specify either the model file itself or the directory containing it. - The script doesn't depend on config.json / params.json, just in case the user downloaded files individually and doesn't have those handy. But you still need tokenizer.model and, for Alpaca, added_tokens.json. - The script tries to give a helpful error message if added_tokens.json is missing.
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python3 -m pip install -r requirements.txt
Include Python dependencies in README (#6)
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readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
# convert the model to ggml FP16 format
python3 convert.py models/mymodel/
Create README.md
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readme : minor (#5204) This is about tuning the code formatting of the README file
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# [Optional] for models using BPE tokenizers
fix typo in readme (#5399) Co-authored-by: Ebey Abraham <ebeyabraham@microsoft.com>
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python convert.py models/mymodel/ --vocab-type bpe
readme : add Aquila-7B model series to supported models (#2487) * support bpe tokenizer in convert Signed-off-by: ldwang <ftgreat@gmail.com> * support bpe tokenizer in convert Signed-off-by: ldwang <ftgreat@gmail.com> * support bpe tokenizer in convert, fix Signed-off-by: ldwang <ftgreat@gmail.com> * Add Aquila-7B models in README.md Signed-off-by: ldwang <ftgreat@gmail.com> * Up Aquila-7B models in README.md Signed-off-by: ldwang <ftgreat@gmail.com> --------- Signed-off-by: ldwang <ftgreat@gmail.com> Co-authored-by: ldwang <ftgreat@gmail.com>
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readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
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# quantize the model to 4-bits (using Q4_K_M method)
./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M
Create README.md
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readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
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# update the gguf filetype to current version if older version is now unsupported
./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY
```
Update README.md (#3289) * Update README.md * Update README.md Co-authored-by: slaren <slarengh@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com>
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readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
### Run the quantized model
Update README.md (#3289) * Update README.md * Update README.md Co-authored-by: slaren <slarengh@gmail.com> --------- Co-authored-by: slaren <slarengh@gmail.com>
2023-09-21 19:00:24 +00:00
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
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```bash
# start inference on a gguf model
./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128
Create README.md
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```
Support all LLaMA models + change Q4_0 quantization storage
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When running the larger models, make sure you have enough disk space to store all the intermediate files.
readme : update hot-topics & models, detail windows release in usage (#3615) * Update README.md * Update README.md * Update README.md * move "Running on Windows" section below "Prepare data and run" --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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### Running on Windows with prebuilt binaries
You will find prebuilt Windows binaries on the release page.
Simply download and extract the latest zip package of choice: (e.g. `llama-b1380-bin-win-avx2-x64.zip`)
From the unzipped folder, open a terminal/cmd window here and place a pre-converted `.gguf` model file. Test out the main example like so:
```
.\main -m llama-2-7b.Q4_0.gguf -n 128
```
Add memory/disk requirements to readme
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### Memory/Disk Requirements
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
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As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same.
Add memory/disk requirements to readme
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readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
| Model | Original size | Quantized size (Q4_0) |
Remove Q4_3 which is no better than Q5 (#1218)
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|------:|--------------:|-----------------------:|
| 7B | 13 GB | 3.9 GB |
| 13B | 24 GB | 7.8 GB |
| 30B | 60 GB | 19.5 GB |
| 65B | 120 GB | 38.5 GB |
Add quantize script for batch quantization (#92) * Add quantize script for batch quantization * Indentation * README for new quantize.sh * Fix script name * Fix file list on Mac OS --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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readme : add quantization info
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### Quantization
Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
readme : update hot topics
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*(outdated)*
ggml : remove bit shuffling (#1405) * ggml : remove Q4_0 bit shufling (ARM NEON) * ggml : remove Q4_1 bit shuffling (ARM NEON + reference) * ggml : nibbles_from_floats() + bytes_from_nibbles() (ARM NEON) * ggml : remove Q4_2 bit shuffling (WIP, BROKEN) * ggml : remove Q5_0 bit shuffling (ARM NEON) * ggml : 2x faster scalar implementations * ggml : remove Q5_1 bit shuffling (ARM NEON + scalar) * ggml : simplify scalar dot * ggml : remove WASM SIMD bit shuffling + remove vzip for ARM 32-bit * ggml : fix Q4_1 quantization * ggml : update cuBLAS + normalize variable names * ggml : remove Q4_2 mode * ggml : minor formatting * ggml : fix Q5_0 quantization * scripts : add script for measuring the time per token * AVX implementations (#1370) * ggml : uniform 5th bit extraction * llama : produce error upon loading old model files * llama : fix model magic/version write * ggml : speed-up Q5_0 + Q5_1 at 4 threads * ggml : preserve old Q4 and Q5 formats * ggml : simplify Q8_1 - no need for low / high sums anymore * ggml : fix Q8_0 and Q8_1 rounding * Revert "AVX implementations (#1370)" This reverts commit 948d124837f9d287d8490f41338e0e4cceb0814f. * ggml : fix AVX2 implementation * sha : update hashes for 7B and 13B * readme : update timings + remove warning banner * llama : update v2 PR number to 1405 * ggml : fix WASM comments * ggml : back to original bit order * readme : add note that Q4 and Q5 have been changed * llama : fix return for unknown version --------- Co-authored-by: Stephan Walter <stephan@walter.name>
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| Model | Measure | F16 | Q4_0 | Q4_1 | Q5_0 | Q5_1 | Q8_0 |
|------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
ggml : use F16 instead of F32 in Q4_0, Q4_1, Q8_0 (#1508) * ggml : use F16 instead of F32 in Q4_0, Q4_1 and Q8_0 * llama : bump LLAMA_FILE_VERSION to 3 * cuda : update Q4 and Q8 dequantize kernels * ggml : fix AVX dot products * readme : update performance table + hot topics
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| 7B | perplexity | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
| 7B | file size | 13.0G | 3.5G | 3.9G | 4.3G | 4.7G | 6.7G |
| 7B | ms/tok @ 4th | 127 | 55 | 54 | 76 | 83 | 72 |
| 7B | ms/tok @ 8th | 122 | 43 | 45 | 52 | 56 | 67 |
| 7B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
| 13B | perplexity | 5.2543 | 5.3860 | 5.3608 | 5.2856 | 5.2706 | 5.2548 |
| 13B | file size | 25.0G | 6.8G | 7.6G | 8.3G | 9.1G | 13G |
| 13B | ms/tok @ 4th | - | 103 | 105 | 148 | 160 | 131 |
| 13B | ms/tok @ 8th | - | 73 | 82 | 98 | 105 | 128 |
| 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 |
readme : add quantization info
2023-04-26 20:24:42 +00:00
readme : add some recent perplexity and bpw measurements to READMES, link for k-quants (#3340) * Update README.md * Update README.md * Update README.md with k-quants bpw measurements
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- [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684)
Update README.md (#5366) Add some links to quantization related PRs
2024-02-06 17:00:16 +00:00
- recent k-quants improvements and new i-quants
readme : add some recent perplexity and bpw measurements to READMES, link for k-quants (#3340) * Update README.md * Update README.md * Update README.md with k-quants bpw measurements
2023-09-27 15:30:36 +00:00
- [#2707](https://github.com/ggerganov/llama.cpp/pull/2707)
- [#2807](https://github.com/ggerganov/llama.cpp/pull/2807)
Update README.md (#5366) Add some links to quantization related PRs
2024-02-06 17:00:16 +00:00
- [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773)
- [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856)
- [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861)
- [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872)
- [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897)
- [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930)
- [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957)
- [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969)
- [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996)
- [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060)
- [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196)
- [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361)
readme : add some recent perplexity and bpw measurements to READMES, link for k-quants (#3340) * Update README.md * Update README.md * Update README.md with k-quants bpw measurements
2023-09-27 15:30:36 +00:00
llama : require first token to be BOS (#1303) * llama : require first token to be BOS * scripts : add ppl-run-all.sh * perplexity : add BOS for each chunk * readme : update perplexity values after BOS fix * perplexity : add clarifying comments
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### Perplexity (measuring model quality)
You can use the `perplexity` example to measure perplexity over a given prompt (lower perplexity is better).
For more information, see [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity).
The perplexity measurements in table above are done against the `wikitext2` test dataset (https://paperswithcode.com/dataset/wikitext-2), with context length of 512.
The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 threads.
readme : update models, cuda + ppl instructions (#3510)
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#### How to run
ci : fix wikitext url + compile warnings (#5569) ggml-ci
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1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
readme : update models, cuda + ppl instructions (#3510)
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2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw`
3. Output:
```
perplexity : calculating perplexity over 655 chunks
24.43 seconds per pass - ETA 4.45 hours
[1]4.5970,[2]5.1807,[3]6.0382,...
```
And after 4.45 hours, you will have the final perplexity.
Add interactive mode (#61) * Initial work on interactive mode. * Improve interactive mode. Make rev. prompt optional. * Update README to explain interactive mode. * Fix OS X build
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### Interactive mode
If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
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In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
Add interactive mode (#61) * Initial work on interactive mode. * Improve interactive mode. Make rev. prompt optional. * Update README to explain interactive mode. * Fix OS X build
2023-03-12 21:13:28 +00:00
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
Here is an example of a few-shot interaction, invoked with the command
Minor style changes
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```bash
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
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# default arguments using a 7B model
Move chat scripts into "./examples"
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./examples/chat.sh
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
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# advanced chat with a 13B model
Move chat scripts into "./examples"
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./examples/chat-13B.sh
Update README.md
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Minor: Readme fixed grammar, spelling, and misc updates (#1071)
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# custom arguments using a 13B model
gguf : new file format with flexible meta data (beta) (#2398) * gguf : first API pass * gguf : read header + meta data * gguf : read tensor info * gguf : initial model loading - not tested * gguf : add gguf_get_tensor_name() * gguf : do not support passing existing ggml_context to gguf_init * gguf : simplify gguf_get_val * gguf : gguf.c is now part of ggml.c * gguf : read / write sample models * gguf : add comments * refactor : reduce code duplication and better API (#2415) * gguf : expose the gguf_type enum through the API for now * gguf : add array support * gguf.py : some code style changes * convert.py : start a new simplified implementation by removing old stuff * convert.py : remove GGML vocab + other obsolete stuff * GGUF : write tensor (#2426) * WIP: Write tensor * GGUF : Support writing tensors in Python * refactor : rm unused import and upd todos * fix : fix errors upd writing example * rm example.gguf * gitignore *.gguf * undo formatting * gguf : add gguf_find_key (#2438) * gguf.cpp : find key example * ggml.h : add gguf_find_key * ggml.c : add gguf_find_key * gguf : fix writing tensors * gguf : do not hardcode tensor names to read * gguf : write sample tensors to read * gguf : add tokenization constants * quick and dirty conversion example * gguf : fix writing gguf arrays * gguf : write tensors one by one and code reuse * gguf : fix writing gguf arrays * gguf : write tensors one by one * gguf : write tensors one by one * gguf : write tokenizer data * gguf : upd gguf conversion script * Update convert-llama-h5-to-gguf.py * gguf : handle already encoded string * ggml.h : get array str and f32 * ggml.c : get arr str and f32 * gguf.py : support any type * Update convert-llama-h5-to-gguf.py * gguf : fix set is not subscriptable * gguf : update convert-llama-h5-to-gguf.py * constants.py : add layer norm eps * gguf.py : add layer norm eps and merges * ggml.h : increase GGML_MAX_NAME to 64 * ggml.c : add gguf_get_arr_n * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Makefile : add gptneox gguf example * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Update convert-llama-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * gguf : support custom alignment value * gguf : fix typo in function call * gguf : mmap tensor data example * fix : update convert-llama-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * convert-gptneox-h5-to-gguf.py : Special tokens * gptneox-main.cpp : special tokens * Update gptneox-main.cpp * constants.py : special tokens * gguf.py : accumulate kv and tensor info data + special tokens * convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens * gguf : gguf counterpart of llama-util.h * gguf-util.h : update note * convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens * convert-llama-h5-to-gguf.py : special tokens * Delete gptneox-common.cpp * Delete gptneox-common.h * convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer * gptneox-main.cpp : gpt2 bpe tokenizer * gpt2 bpe tokenizer (handles merges and unicode) * Makefile : remove gptneox-common * gguf.py : bytesarray for gpt2bpe tokenizer * cmpnct_gpt2bpe.hpp : comments * gguf.py : use custom alignment if present * gguf : minor stuff * Update gptneox-main.cpp * map tensor names * convert-gptneox-h5-to-gguf.py : map tensor names * convert-llama-h5-to-gguf.py : map tensor names * gptneox-main.cpp : map tensor names * gguf : start implementing libllama in GGUF (WIP) * gguf : start implementing libllama in GGUF (WIP) * rm binary commited by mistake * upd .gitignore * gguf : calculate n_mult * gguf : inference with 7B model working (WIP) * gguf : rm deprecated function * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : add gguf_get_kv_type * gguf : add gguf_get_kv_type * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver * gguf : rm references to old file formats * gguf : shorter name for member variable * gguf : rm redundant method * gguf : get rid of n_mult, read n_ff from file * Update gguf_tensor_map.py * Update gptneox-main.cpp * gguf : rm references to old file magics * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : quantization is working * gguf : roper closing of file * gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : no need to convert tensors twice * convert-llama-h5-to-gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : simplify nbytes * convert-llama-h5-to-gguf.py : simplify nbytes * gptneox-main.cpp : n_layer --> n_block * constants.py : n_layer --> n_block * gguf.py : n_layer --> n_block * convert-gptneox-h5-to-gguf.py : n_layer --> n_block * convert-llama-h5-to-gguf.py : n_layer --> n_block * gptneox-main.cpp : n_layer --> n_block * Update gguf_tensor_map.py * convert-gptneox-h5-to-gguf.py : load model in parts to save memory * convert-llama-h5-to-gguf.py : load model in parts to save memory * convert : write more metadata for LLaMA * convert : rm quantization version * convert-gptneox-h5-to-gguf.py : add file_type key * gptneox-main.cpp : add file_type key * fix conflicts * gguf : add todos and comments * convert-gptneox-h5-to-gguf.py : tensor name map changes * Create gguf_namemap.py : tensor name map changes * Delete gguf_tensor_map.py * gptneox-main.cpp : tensor name map changes * convert-llama-h5-to-gguf.py : fixes * gguf.py : dont add empty strings * simple : minor style changes * gguf : use UNIX line ending * Create convert-llama-7b-pth-to-gguf.py * llama : sync gguf-llama.cpp with latest llama.cpp (#2608) * llama : sync gguf-llama.cpp with latest llama.cpp * minor : indentation + assert * llama : refactor gguf_buffer and gguf_ctx_buffer * llama : minor * gitignore : add gptneox-main * llama : tokenizer fixes (#2549) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * convert : update convert-new.py with tokenizer fixes (#2614) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * llama : sync gguf-llama with llama (#2613) * llama : sync gguf-llama with llama * tests : fix build + warnings (test-tokenizer-1 still fails) * tests : fix wstring_convert * convert : fix layer names * llama : sync gguf-llama.cpp * convert : update HF converter to new tokenizer voodoo magics * llama : update tokenizer style * convert-llama-h5-to-gguf.py : add token types * constants.py : add token types * gguf.py : add token types * convert-llama-7b-pth-to-gguf.py : add token types * gguf-llama.cpp : fix n_head_kv * convert-llama-h5-to-gguf.py : add 70b gqa support * gguf.py : add tensor data layout * convert-llama-h5-to-gguf.py : add tensor data layout * convert-llama-7b-pth-to-gguf.py : add tensor data layout * gptneox-main.cpp : add tensor data layout * convert-llama-h5-to-gguf.py : clarify the reverse permute * llama : refactor model loading code (#2620) * llama : style formatting + remove helper methods * llama : fix quantization using gguf tool * llama : simplify gguf_file_saver * llama : fix method names * llama : simplify write_header() * llama : no need to pass full file loader to the file saver just gguf_ctx * llama : gguf_file_saver write I32 * llama : refactor tensor names (#2622) * gguf: update tensor names searched in quantization * gguf : define tensor names as constants * gguf : initial write API (not tested yet) * gguf : write to file API (not tested) * gguf : initial write API ready + example * gguf : fix header write * gguf : fixes + simplify example + add ggml_nbytes_pad() * gguf : minor * llama : replace gguf_file_saver with new gguf write API * gguf : streaming support when writing files * gguf : remove oboslete write methods * gguf : remove obosolete gguf_get_arr_xxx API * llama : simplify gguf_file_loader * llama : move hparams and vocab from gguf_file_loader to llama_model_loader * llama : merge gguf-util.h in llama.cpp * llama : reorder definitions in .cpp to match .h * llama : minor simplifications * llama : refactor llama_model_loader (WIP) wip : remove ggml_ctx from llama_model_loader wip : merge gguf_file_loader in llama_model_loader * llama : fix shape prints * llama : fix Windows build + fix norm_rms_eps key * llama : throw error on missing KV paris in model meta data * llama : improve printing + log meta data * llama : switch print order of meta data --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> * gguf : deduplicate (#2629) * gguf : better type names * dedup : CPU + Metal is working * ggml : fix warnings about unused results * llama.cpp : fix line feed and compiler warning * llama : fix strncpy warning + note token_to_str does not write null * llama : restore the original load/save session implementation Will migrate this to GGUF in the future * convert-llama-h5-to-gguf.py : support alt ctx param name * ggml : assert when using ggml_mul with non-F32 src1 * examples : dedup simple --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> * gguf.py : merge all files in gguf.py * convert-new.py : pick #2427 for HF 70B support * examples/gguf : no need to keep q option for quantization any more * llama.cpp : print actual model size * llama.cpp : use ggml_elements() * convert-new.py : output gguf (#2635) * convert-new.py : output gguf (WIP) * convert-new.py : add gguf key-value pairs * llama : add hparams.ctx_train + no longer print ftype * convert-new.py : minor fixes * convert-new.py : vocab-only option should work now * llama : fix tokenizer to use llama_char_to_byte * tests : add new ggml-vocab-llama.gguf * convert-new.py : tensor name mapping * convert-new.py : add map for skipping tensor serialization * convert-new.py : convert script now works * gguf.py : pick some of the refactoring from #2644 * convert-new.py : minor fixes * convert.py : update to support GGUF output * Revert "ci : disable CI temporary to not waste energy" This reverts commit 7e82d25f40386540c2c15226300ad998ecd871ea. * convert.py : n_head_kv optional and .gguf file extension * convert.py : better always have n_head_kv and default it to n_head * llama : sync with recent PRs on master * editorconfig : ignore models folder ggml-ci * ci : update ".bin" to ".gguf" extension ggml-ci * llama : fix llama_model_loader memory leak * gptneox : move as a WIP example * llama : fix lambda capture ggml-ci * ggml : fix bug in gguf_set_kv ggml-ci * common.h : .bin --> .gguf * quantize-stats.cpp : .bin --> .gguf * convert.py : fix HF tensor permuting / unpacking ggml-ci * llama.cpp : typo * llama : throw error if gguf fails to init from file ggml-ci * llama : fix tensor name grepping during quantization ggml-ci * gguf.py : write tensors in a single pass (#2644) * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : style fixes in simple conversion script * gguf : refactor gptneox conversion script * gguf : rename h5 to hf (for HuggingFace) * gguf : refactor pth to gguf conversion script * gguf : rm file_type key and method * gguf.py : fix vertical alignment * gguf.py : indentation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * convert-gptneox-hf-to-gguf.py : fixes * gguf.py : gptneox mapping * convert-llama-hf-to-gguf.py : fixes * convert-llama-7b-pth-to-gguf.py : fixes * ggml.h : reverse GGUF_MAGIC * gguf.py : reverse GGUF_MAGIC * test-tokenizer-0.cpp : fix warning * llama.cpp : print kv general.name * llama.cpp : get special token kv and linefeed token id * llama : print number of tensors per type + print arch + style * tests : update vocab file with new magic * editorconfig : fix whitespaces * llama : re-order functions * llama : remove C++ API + reorganize common source in /common dir * llama : minor API updates * llama : avoid hardcoded special tokens * llama : fix MPI build ggml-ci * llama : introduce enum llama_vocab_type + remove hardcoded string constants * convert-falcon-hf-to-gguf.py : falcon HF --> gguf conversion, not tested * falcon-main.cpp : falcon inference example * convert-falcon-hf-to-gguf.py : remove extra kv * convert-gptneox-hf-to-gguf.py : remove extra kv * convert-llama-7b-pth-to-gguf.py : remove extra kv * convert-llama-hf-to-gguf.py : remove extra kv * gguf.py : fix for falcon 40b * falcon-main.cpp : fix for falcon 40b * convert-falcon-hf-to-gguf.py : update ref * convert-falcon-hf-to-gguf.py : add tensor data layout * cmpnct_gpt2bpe.hpp : fixes * falcon-main.cpp : fixes * gptneox-main.cpp : fixes * cmpnct_gpt2bpe.hpp : remove non-general stuff * Update examples/server/README.md Co-authored-by: slaren <slarengh@gmail.com> * cmpnct_gpt2bpe.hpp : cleanup * convert-llama-hf-to-gguf.py : special tokens * convert-llama-7b-pth-to-gguf.py : special tokens * convert-permute-debug.py : permute debug print * convert-permute-debug-master.py : permute debug for master * convert-permute-debug.py : change permute type of attn_q * convert.py : 70b model working (change attn_q permute) * Delete convert-permute-debug-master.py * Delete convert-permute-debug.py * convert-llama-hf-to-gguf.py : fix attn_q permute * gguf.py : fix rope scale kv * convert-llama-hf-to-gguf.py : rope scale and added tokens * convert-llama-7b-pth-to-gguf.py : rope scale and added tokens * llama.cpp : use rope scale kv * convert-llama-7b-pth-to-gguf.py : rope scale fix * convert-llama-hf-to-gguf.py : rope scale fix * py : fix whitespace * gguf : add Python script to convert GGMLv3 LLaMA models to GGUF (#2682) * First pass at converting GGMLv3 LLaMA models to GGUF * Cleanups, better output during conversion * Fix vocab space conversion logic * More vocab conversion fixes * Add description to converted GGUF files * Improve help text, expand warning * Allow specifying name and description for output GGUF * Allow overriding vocab and hyperparams from original model metadata * Use correct params override var name * Fix wrong type size for Q8_K Better handling of original style metadata * Set default value for gguf add_tensor raw_shape KW arg * llama : improve token type support (#2668) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * llama : add API for token type ggml-ci * tests : use new tokenizer type API (#2692) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * Improve commentary * Use token type API in test-tokenizer-1.cpp * py : cosmetics * readme : add notice about new file format ggml-ci --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> Co-authored-by: goerch <jhr.walter@t-online.de> Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
2023-08-21 20:07:43 +00:00
./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
Add interactive mode (#61) * Initial work on interactive mode. * Improve interactive mode. Make rev. prompt optional. * Update README to explain interactive mode. * Fix OS X build
2023-03-12 21:13:28 +00:00
```
Minor style changes
2023-03-21 16:10:32 +00:00
examples/main README improvements and some light refactoring (#1131)
2023-04-24 15:45:32 +00:00
Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `main` example program.
Add interactive mode (#61) * Initial work on interactive mode. * Improve interactive mode. Make rev. prompt optional. * Update README to explain interactive mode. * Fix OS X build
2023-03-12 21:13:28 +00:00
Update README.md
2023-03-12 21:39:01 +00:00
![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png)
Add interactive mode (#61) * Initial work on interactive mode. * Improve interactive mode. Make rev. prompt optional. * Update README to explain interactive mode. * Fix OS X build
2023-03-12 21:13:28 +00:00
readme : add docs for chat-persistent.sh (#1568) * readme : add docs for chat-persistent.sh * Update README.md
2023-05-24 06:24:01 +00:00
### Persistent Interaction
The prompt, user inputs, and model generations can be saved and resumed across calls to `./main` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file.
```bash
# Start a new chat
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh
# Resume that chat
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/default ./examples/chat-persistent.sh
# Start a different chat with the same prompt/model
PROMPT_CACHE_FILE=chat.prompt.bin CHAT_SAVE_DIR=./chat/another ./examples/chat-persistent.sh
# Different prompt cache for different prompt/model
PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh
```
docs : add grammar docs (#2701) * docs : add grammar docs * tweaks to grammar guide * rework GBNF example to be a commented grammar
2023-08-23 01:01:57 +00:00
### Constrained output with grammars
`llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only:
```bash
./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
```
The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md).
readme : add link to grammars app (#3388) * Add link to grammars app per @ggernagov suggestion Adding a sentence in the Grammars section of README to point to grammar app, per https://github.com/ggerganov/llama.cpp/discussions/2494#discussioncomment-7138211 * Update README.md
2023-09-29 11:15:57 +00:00
For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one.
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
### Instruct mode
Add instruction for using Alpaca (#240)
2023-03-19 16:49:50 +00:00
readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md
2024-02-07 06:21:30 +00:00
1. First, download and place the `ggml` model into the `./models` folder
Move model section from issue template to README.md (#421) * Update custom.md * Removed Model section as it is better placed in README.md * Updates to README.md model section * Inserted text that was removed from issue template about obtaining models from FB and links to papers describing the various models * Removed IPF down links for the Alpaca 7B models as these look to be in the old data format and probably shouldn't be directly linked to, anyway * Updated the perplexity section to point at Perplexity scores #406 discussion
2023-03-23 11:30:40 +00:00
2. Run the `main` tool like this:
Add instruction for using Alpaca (#240)
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```
Move chat scripts into "./examples"
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./examples/alpaca.sh
Add instruction for using Alpaca (#240)
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```
Sample run:
```
== Running in interactive mode. ==
- Press Ctrl+C to interject at any time.
- Press Return to return control to LLaMa.
- If you want to submit another line, end your input in '\'.
Below is an instruction that describes a task. Write a response that appropriately completes the request.
> How many letters are there in the English alphabet?
There 26 letters in the English Alphabet
> What is the most common way of transportation in Amsterdam?
The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
Fix convert script, warnings alpaca instructions, default params
2023-03-21 15:59:16 +00:00
> List 5 words that start with "ca".
Add instruction for using Alpaca (#240)
2023-03-19 16:49:50 +00:00
cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
Fix whitespace, add .editorconfig, add GitHub workflow (#883)
2023-04-11 19:45:44 +00:00
>
Add instruction for using Alpaca (#240)
2023-03-19 16:49:50 +00:00
```
Obtaining LLaMA 2 instructions (#2308) * Obtaining LLaMA 2 instructions * Removed sharing warning for LLaMA 2 * Linked TheBloke's GGML repos * Add LLaMA 2 to list of supported models * Added LLaMA 2 usage instructions * Added links to LLaMA 2 70B models
2023-07-28 01:14:11 +00:00
### Obtaining and using the Facebook LLaMA 2 model
- Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data.
- Alternatively, if you want to save time and space, you can download already converted and quantized models from [TheBloke](https://huggingface.co/TheBloke), including:
Update deprecated GGML TheBloke links to GGUF (#3079)
2023-09-08 10:32:55 +00:00
- [LLaMA 2 7B base](https://huggingface.co/TheBloke/Llama-2-7B-GGUF)
- [LLaMA 2 13B base](https://huggingface.co/TheBloke/Llama-2-13B-GGUF)
- [LLaMA 2 70B base](https://huggingface.co/TheBloke/Llama-2-70B-GGUF)
- [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGUF)
- [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF)
- [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF)
Obtaining LLaMA 2 instructions (#2308) * Obtaining LLaMA 2 instructions * Removed sharing warning for LLaMA 2 * Linked TheBloke's GGML repos * Add LLaMA 2 to list of supported models * Added LLaMA 2 usage instructions * Added links to LLaMA 2 70B models
2023-07-28 01:14:11 +00:00
scripts : platform independent script to verify sha256 checksums (#1203) * python script to verify the checksum of the llama models Added Python script for verifying SHA256 checksums of files in a directory, which can run on multiple platforms. Improved the formatting of the output results for better readability. * Update README.md update to the readme for improved readability and to explain the usage of the python checksum verification script * update the verification script I've extended the script based on suggestions by @prusnak The script now checks the available RAM, is there is enough to check the file at once it will do so. If not the file is read in chunks. * minor improvment small change so that the available ram is checked and not the total ram * remove the part of the code that reads the file at once if enough ram is available based on suggestions from @prusnak i removed the part of the code that checks whether the user had enough ram to read the entire model at once. the file is now always read in chunks. * Update verify-checksum-models.py quick fix to pass the git check
2023-05-03 15:31:28 +00:00
### Seminal papers and background on the models
Add SHA256SUMS file and instructions to README how to obtain and verify the downloads Hashes created using: sha256sum models/*B/*.pth models/*[7136]B/ggml-model-f16.bin* models/*[7136]B/ggml-model-q4_0.bin* > SHA256SUMS
2023-03-20 20:14:06 +00:00
scripts : platform independent script to verify sha256 checksums (#1203) * python script to verify the checksum of the llama models Added Python script for verifying SHA256 checksums of files in a directory, which can run on multiple platforms. Improved the formatting of the output results for better readability. * Update README.md update to the readme for improved readability and to explain the usage of the python checksum verification script * update the verification script I've extended the script based on suggestions by @prusnak The script now checks the available RAM, is there is enough to check the file at once it will do so. If not the file is read in chunks. * minor improvment small change so that the available ram is checked and not the total ram * remove the part of the code that reads the file at once if enough ram is available based on suggestions from @prusnak i removed the part of the code that checks whether the user had enough ram to read the entire model at once. the file is now always read in chunks. * Update verify-checksum-models.py quick fix to pass the git check
2023-05-03 15:31:28 +00:00
If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
- LLaMA:
scripts : platform independent script to verify sha256 checksums (#1203) * python script to verify the checksum of the llama models Added Python script for verifying SHA256 checksums of files in a directory, which can run on multiple platforms. Improved the formatting of the output results for better readability. * Update README.md update to the readme for improved readability and to explain the usage of the python checksum verification script * update the verification script I've extended the script based on suggestions by @prusnak The script now checks the available RAM, is there is enough to check the file at once it will do so. If not the file is read in chunks. * minor improvment small change so that the available ram is checked and not the total ram * remove the part of the code that reads the file at once if enough ram is available based on suggestions from @prusnak i removed the part of the code that checks whether the user had enough ram to read the entire model at once. the file is now always read in chunks. * Update verify-checksum-models.py quick fix to pass the git check
2023-05-03 15:31:28 +00:00
- [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
- [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
- GPT-3
scripts : platform independent script to verify sha256 checksums (#1203) * python script to verify the checksum of the llama models Added Python script for verifying SHA256 checksums of files in a directory, which can run on multiple platforms. Improved the formatting of the output results for better readability. * Update README.md update to the readme for improved readability and to explain the usage of the python checksum verification script * update the verification script I've extended the script based on suggestions by @prusnak The script now checks the available RAM, is there is enough to check the file at once it will do so. If not the file is read in chunks. * minor improvment small change so that the available ram is checked and not the total ram * remove the part of the code that reads the file at once if enough ram is available based on suggestions from @prusnak i removed the part of the code that checks whether the user had enough ram to read the entire model at once. the file is now always read in chunks. * Update verify-checksum-models.py quick fix to pass the git check
2023-05-03 15:31:28 +00:00
- [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
- GPT-3.5 / InstructGPT / ChatGPT:
scripts : platform independent script to verify sha256 checksums (#1203) * python script to verify the checksum of the llama models Added Python script for verifying SHA256 checksums of files in a directory, which can run on multiple platforms. Improved the formatting of the output results for better readability. * Update README.md update to the readme for improved readability and to explain the usage of the python checksum verification script * update the verification script I've extended the script based on suggestions by @prusnak The script now checks the available RAM, is there is enough to check the file at once it will do so. If not the file is read in chunks. * minor improvment small change so that the available ram is checked and not the total ram * remove the part of the code that reads the file at once if enough ram is available based on suggestions from @prusnak i removed the part of the code that checks whether the user had enough ram to read the entire model at once. the file is now always read in chunks. * Update verify-checksum-models.py quick fix to pass the git check
2023-05-03 15:31:28 +00:00
- [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
- [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
Fix whitespace, add .editorconfig, add GitHub workflow (#883)
2023-04-11 19:45:44 +00:00
Add section to README on how to run the project on Android (#130)
2023-03-14 13:30:08 +00:00
### Android
readme : alternative way to build for Android with CLBlast. (#1828)
2023-06-17 09:01:06 +00:00
#### Building the Project using Android NDK
readme: replace termux links with homepage, play store is deprecated (#680)
2023-04-01 14:57:30 +00:00
You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/).
readme : update Android build instructions (#1922) Add steps for using termux on android devices to prevent common errors.
2023-06-18 08:28:26 +00:00
First, install the essential packages for termux:
```
pkg install clang wget git cmake
```
Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
Add section to README on how to run the project on Android (#130)
2023-03-14 13:30:08 +00:00
```
$ mkdir build-android
$ cd build-android
$ export NDK=<your_ndk_directory>
$ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
$ make
```
readme: replace termux links with homepage, play store is deprecated (#680)
2023-04-01 14:57:30 +00:00
Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card.
Add section to README on how to run the project on Android (#130)
2023-03-14 13:30:08 +00:00
Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
readme : alternative way to build for Android with CLBlast. (#1828)
2023-06-17 09:01:06 +00:00
#### Building the Project using Termux (F-Droid)
Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.
Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.
If you opt to utilize OpenBLAS, you'll need to install the corresponding package.
```
apt install libopenblas
```
Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:
```
apt install ocl-icd opencl-headers opencl-clhpp clinfo
```
In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:
```
cmake .
make
cp libclblast.so* $PREFIX/lib
cp ./include/clblast.h ../llama.cpp
```
Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:
```
cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)
```
Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:
```
GGML_OPENCL_PLATFORM=0
GGML_OPENCL_DEVICE=0
readme : fixed termux instructions (#1973)
2023-06-24 10:32:13 +00:00
export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH
readme : alternative way to build for Android with CLBlast. (#1828)
2023-06-17 09:01:06 +00:00
```
readme : LD_LIBRARY_PATH complement for some Android devices when building with CLBlast inside Termux (#2007) * docs - Alternative way to build at Android, with CLBlast. * doc - LD_LIBRARY_PATH complement for some Android devices when building with CLBlast inside Termux. * doc- fix typo
2023-06-26 19:34:45 +00:00
(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )
readme : alternative way to build for Android with CLBlast. (#1828)
2023-06-17 09:01:06 +00:00
For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.
readme : update Termux instructions (#2147) The file pathing is significant when running models inside of Termux on Android devices. llama.cpp performance is improved with loading a .bin from the $HOME directory.
2023-07-09 08:20:43 +00:00
Place your desired model into the `~/llama.cpp/models/` directory and execute the `./main (...)` script.
readme : fixed termux instructions (#1973)
2023-06-24 10:32:13 +00:00
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
### Docker
#### Prerequisites
* Docker must be installed and running on your system.
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
* Create a folder to store big models & intermediate files (ex. /llama/models)
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
#### Images
docker : add server-first container images (#5157) * feat: add Dockerfiles for each platform that user ./server instead of ./main * feat: update .github/workflows/docker.yml to build server-first docker containers * doc: add information about running the server with Docker to README.md * doc: add information about running with docker to the server README * doc: update n-gpu-layers to show correct GPU usage * fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA
2024-01-28 07:55:31 +00:00
We have three Docker images available for this project:
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
docker : add gpu image CI builds (#3103) Enables the GPU enabled container images to be built and pushed alongside the CPU containers. Co-authored-by: canardleteer <eris.has.a.dad+github@gmail.com>
2023-09-14 16:47:00 +00:00
1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`)
2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`)
readme : fix typo (#5490) executabhle -> executable
2024-02-14 15:15:49 +00:00
3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executable file. (platforms: `linux/amd64`, `linux/arm64`)
docker : add gpu image CI builds (#3103) Enables the GPU enabled container images to be built and pushed alongside the CPU containers. Co-authored-by: canardleteer <eris.has.a.dad+github@gmail.com>
2023-09-14 16:47:00 +00:00
Additionally, there the following images, similar to the above:
- `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`)
- `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`)
docker : add server-first container images (#5157) * feat: add Dockerfiles for each platform that user ./server instead of ./main * feat: update .github/workflows/docker.yml to build server-first docker containers * doc: add information about running the server with Docker to README.md * doc: add information about running with docker to the server README * doc: update n-gpu-layers to show correct GPU usage * fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA
2024-01-28 07:55:31 +00:00
- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`)
docker : add gpu image CI builds (#3103) Enables the GPU enabled container images to be built and pushed alongside the CPU containers. Co-authored-by: canardleteer <eris.has.a.dad+github@gmail.com>
2023-09-14 16:47:00 +00:00
- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
docker : add server-first container images (#5157) * feat: add Dockerfiles for each platform that user ./server instead of ./main * feat: update .github/workflows/docker.yml to build server-first docker containers * doc: add information about running the server with Docker to README.md * doc: add information about running with docker to the server README * doc: update n-gpu-layers to show correct GPU usage * fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA
2024-01-28 07:55:31 +00:00
- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
docker : add gpu image CI builds (#3103) Enables the GPU enabled container images to be built and pushed alongside the CPU containers. Co-authored-by: canardleteer <eris.has.a.dad+github@gmail.com>
2023-09-14 16:47:00 +00:00
readme : fix typo (#4253) llama.cpp uses GitHub Actions, not Gitlab Actions.
2023-11-30 21:43:32 +00:00
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
#### Usage
The easiest way to download the models, convert them to ggml and optimize them is with the --all-in-one command which includes the full docker image.
Make docker instructions more explicit (#785)
2023-04-06 06:56:58 +00:00
Replace `/path/to/models` below with the actual path where you downloaded the models.
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
```bash
Make docker instructions more explicit (#785)
2023-04-06 06:56:58 +00:00
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --all-in-one "/models/" 7B
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
```
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
On completion, you are ready to play!
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
```bash
gguf : new file format with flexible meta data (beta) (#2398) * gguf : first API pass * gguf : read header + meta data * gguf : read tensor info * gguf : initial model loading - not tested * gguf : add gguf_get_tensor_name() * gguf : do not support passing existing ggml_context to gguf_init * gguf : simplify gguf_get_val * gguf : gguf.c is now part of ggml.c * gguf : read / write sample models * gguf : add comments * refactor : reduce code duplication and better API (#2415) * gguf : expose the gguf_type enum through the API for now * gguf : add array support * gguf.py : some code style changes * convert.py : start a new simplified implementation by removing old stuff * convert.py : remove GGML vocab + other obsolete stuff * GGUF : write tensor (#2426) * WIP: Write tensor * GGUF : Support writing tensors in Python * refactor : rm unused import and upd todos * fix : fix errors upd writing example * rm example.gguf * gitignore *.gguf * undo formatting * gguf : add gguf_find_key (#2438) * gguf.cpp : find key example * ggml.h : add gguf_find_key * ggml.c : add gguf_find_key * gguf : fix writing tensors * gguf : do not hardcode tensor names to read * gguf : write sample tensors to read * gguf : add tokenization constants * quick and dirty conversion example * gguf : fix writing gguf arrays * gguf : write tensors one by one and code reuse * gguf : fix writing gguf arrays * gguf : write tensors one by one * gguf : write tensors one by one * gguf : write tokenizer data * gguf : upd gguf conversion script * Update convert-llama-h5-to-gguf.py * gguf : handle already encoded string * ggml.h : get array str and f32 * ggml.c : get arr str and f32 * gguf.py : support any type * Update convert-llama-h5-to-gguf.py * gguf : fix set is not subscriptable * gguf : update convert-llama-h5-to-gguf.py * constants.py : add layer norm eps * gguf.py : add layer norm eps and merges * ggml.h : increase GGML_MAX_NAME to 64 * ggml.c : add gguf_get_arr_n * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Makefile : add gptneox gguf example * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Update convert-llama-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * gguf : support custom alignment value * gguf : fix typo in function call * gguf : mmap tensor data example * fix : update convert-llama-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * convert-gptneox-h5-to-gguf.py : Special tokens * gptneox-main.cpp : special tokens * Update gptneox-main.cpp * constants.py : special tokens * gguf.py : accumulate kv and tensor info data + special tokens * convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens * gguf : gguf counterpart of llama-util.h * gguf-util.h : update note * convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens * convert-llama-h5-to-gguf.py : special tokens * Delete gptneox-common.cpp * Delete gptneox-common.h * convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer * gptneox-main.cpp : gpt2 bpe tokenizer * gpt2 bpe tokenizer (handles merges and unicode) * Makefile : remove gptneox-common * gguf.py : bytesarray for gpt2bpe tokenizer * cmpnct_gpt2bpe.hpp : comments * gguf.py : use custom alignment if present * gguf : minor stuff * Update gptneox-main.cpp * map tensor names * convert-gptneox-h5-to-gguf.py : map tensor names * convert-llama-h5-to-gguf.py : map tensor names * gptneox-main.cpp : map tensor names * gguf : start implementing libllama in GGUF (WIP) * gguf : start implementing libllama in GGUF (WIP) * rm binary commited by mistake * upd .gitignore * gguf : calculate n_mult * gguf : inference with 7B model working (WIP) * gguf : rm deprecated function * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : add gguf_get_kv_type * gguf : add gguf_get_kv_type * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver * gguf : rm references to old file formats * gguf : shorter name for member variable * gguf : rm redundant method * gguf : get rid of n_mult, read n_ff from file * Update gguf_tensor_map.py * Update gptneox-main.cpp * gguf : rm references to old file magics * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : quantization is working * gguf : roper closing of file * gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : no need to convert tensors twice * convert-llama-h5-to-gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : simplify nbytes * convert-llama-h5-to-gguf.py : simplify nbytes * gptneox-main.cpp : n_layer --> n_block * constants.py : n_layer --> n_block * gguf.py : n_layer --> n_block * convert-gptneox-h5-to-gguf.py : n_layer --> n_block * convert-llama-h5-to-gguf.py : n_layer --> n_block * gptneox-main.cpp : n_layer --> n_block * Update gguf_tensor_map.py * convert-gptneox-h5-to-gguf.py : load model in parts to save memory * convert-llama-h5-to-gguf.py : load model in parts to save memory * convert : write more metadata for LLaMA * convert : rm quantization version * convert-gptneox-h5-to-gguf.py : add file_type key * gptneox-main.cpp : add file_type key * fix conflicts * gguf : add todos and comments * convert-gptneox-h5-to-gguf.py : tensor name map changes * Create gguf_namemap.py : tensor name map changes * Delete gguf_tensor_map.py * gptneox-main.cpp : tensor name map changes * convert-llama-h5-to-gguf.py : fixes * gguf.py : dont add empty strings * simple : minor style changes * gguf : use UNIX line ending * Create convert-llama-7b-pth-to-gguf.py * llama : sync gguf-llama.cpp with latest llama.cpp (#2608) * llama : sync gguf-llama.cpp with latest llama.cpp * minor : indentation + assert * llama : refactor gguf_buffer and gguf_ctx_buffer * llama : minor * gitignore : add gptneox-main * llama : tokenizer fixes (#2549) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * convert : update convert-new.py with tokenizer fixes (#2614) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * llama : sync gguf-llama with llama (#2613) * llama : sync gguf-llama with llama * tests : fix build + warnings (test-tokenizer-1 still fails) * tests : fix wstring_convert * convert : fix layer names * llama : sync gguf-llama.cpp * convert : update HF converter to new tokenizer voodoo magics * llama : update tokenizer style * convert-llama-h5-to-gguf.py : add token types * constants.py : add token types * gguf.py : add token types * convert-llama-7b-pth-to-gguf.py : add token types * gguf-llama.cpp : fix n_head_kv * convert-llama-h5-to-gguf.py : add 70b gqa support * gguf.py : add tensor data layout * convert-llama-h5-to-gguf.py : add tensor data layout * convert-llama-7b-pth-to-gguf.py : add tensor data layout * gptneox-main.cpp : add tensor data layout * convert-llama-h5-to-gguf.py : clarify the reverse permute * llama : refactor model loading code (#2620) * llama : style formatting + remove helper methods * llama : fix quantization using gguf tool * llama : simplify gguf_file_saver * llama : fix method names * llama : simplify write_header() * llama : no need to pass full file loader to the file saver just gguf_ctx * llama : gguf_file_saver write I32 * llama : refactor tensor names (#2622) * gguf: update tensor names searched in quantization * gguf : define tensor names as constants * gguf : initial write API (not tested yet) * gguf : write to file API (not tested) * gguf : initial write API ready + example * gguf : fix header write * gguf : fixes + simplify example + add ggml_nbytes_pad() * gguf : minor * llama : replace gguf_file_saver with new gguf write API * gguf : streaming support when writing files * gguf : remove oboslete write methods * gguf : remove obosolete gguf_get_arr_xxx API * llama : simplify gguf_file_loader * llama : move hparams and vocab from gguf_file_loader to llama_model_loader * llama : merge gguf-util.h in llama.cpp * llama : reorder definitions in .cpp to match .h * llama : minor simplifications * llama : refactor llama_model_loader (WIP) wip : remove ggml_ctx from llama_model_loader wip : merge gguf_file_loader in llama_model_loader * llama : fix shape prints * llama : fix Windows build + fix norm_rms_eps key * llama : throw error on missing KV paris in model meta data * llama : improve printing + log meta data * llama : switch print order of meta data --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> * gguf : deduplicate (#2629) * gguf : better type names * dedup : CPU + Metal is working * ggml : fix warnings about unused results * llama.cpp : fix line feed and compiler warning * llama : fix strncpy warning + note token_to_str does not write null * llama : restore the original load/save session implementation Will migrate this to GGUF in the future * convert-llama-h5-to-gguf.py : support alt ctx param name * ggml : assert when using ggml_mul with non-F32 src1 * examples : dedup simple --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> * gguf.py : merge all files in gguf.py * convert-new.py : pick #2427 for HF 70B support * examples/gguf : no need to keep q option for quantization any more * llama.cpp : print actual model size * llama.cpp : use ggml_elements() * convert-new.py : output gguf (#2635) * convert-new.py : output gguf (WIP) * convert-new.py : add gguf key-value pairs * llama : add hparams.ctx_train + no longer print ftype * convert-new.py : minor fixes * convert-new.py : vocab-only option should work now * llama : fix tokenizer to use llama_char_to_byte * tests : add new ggml-vocab-llama.gguf * convert-new.py : tensor name mapping * convert-new.py : add map for skipping tensor serialization * convert-new.py : convert script now works * gguf.py : pick some of the refactoring from #2644 * convert-new.py : minor fixes * convert.py : update to support GGUF output * Revert "ci : disable CI temporary to not waste energy" This reverts commit 7e82d25f40386540c2c15226300ad998ecd871ea. * convert.py : n_head_kv optional and .gguf file extension * convert.py : better always have n_head_kv and default it to n_head * llama : sync with recent PRs on master * editorconfig : ignore models folder ggml-ci * ci : update ".bin" to ".gguf" extension ggml-ci * llama : fix llama_model_loader memory leak * gptneox : move as a WIP example * llama : fix lambda capture ggml-ci * ggml : fix bug in gguf_set_kv ggml-ci * common.h : .bin --> .gguf * quantize-stats.cpp : .bin --> .gguf * convert.py : fix HF tensor permuting / unpacking ggml-ci * llama.cpp : typo * llama : throw error if gguf fails to init from file ggml-ci * llama : fix tensor name grepping during quantization ggml-ci * gguf.py : write tensors in a single pass (#2644) * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : style fixes in simple conversion script * gguf : refactor gptneox conversion script * gguf : rename h5 to hf (for HuggingFace) * gguf : refactor pth to gguf conversion script * gguf : rm file_type key and method * gguf.py : fix vertical alignment * gguf.py : indentation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * convert-gptneox-hf-to-gguf.py : fixes * gguf.py : gptneox mapping * convert-llama-hf-to-gguf.py : fixes * convert-llama-7b-pth-to-gguf.py : fixes * ggml.h : reverse GGUF_MAGIC * gguf.py : reverse GGUF_MAGIC * test-tokenizer-0.cpp : fix warning * llama.cpp : print kv general.name * llama.cpp : get special token kv and linefeed token id * llama : print number of tensors per type + print arch + style * tests : update vocab file with new magic * editorconfig : fix whitespaces * llama : re-order functions * llama : remove C++ API + reorganize common source in /common dir * llama : minor API updates * llama : avoid hardcoded special tokens * llama : fix MPI build ggml-ci * llama : introduce enum llama_vocab_type + remove hardcoded string constants * convert-falcon-hf-to-gguf.py : falcon HF --> gguf conversion, not tested * falcon-main.cpp : falcon inference example * convert-falcon-hf-to-gguf.py : remove extra kv * convert-gptneox-hf-to-gguf.py : remove extra kv * convert-llama-7b-pth-to-gguf.py : remove extra kv * convert-llama-hf-to-gguf.py : remove extra kv * gguf.py : fix for falcon 40b * falcon-main.cpp : fix for falcon 40b * convert-falcon-hf-to-gguf.py : update ref * convert-falcon-hf-to-gguf.py : add tensor data layout * cmpnct_gpt2bpe.hpp : fixes * falcon-main.cpp : fixes * gptneox-main.cpp : fixes * cmpnct_gpt2bpe.hpp : remove non-general stuff * Update examples/server/README.md Co-authored-by: slaren <slarengh@gmail.com> * cmpnct_gpt2bpe.hpp : cleanup * convert-llama-hf-to-gguf.py : special tokens * convert-llama-7b-pth-to-gguf.py : special tokens * convert-permute-debug.py : permute debug print * convert-permute-debug-master.py : permute debug for master * convert-permute-debug.py : change permute type of attn_q * convert.py : 70b model working (change attn_q permute) * Delete convert-permute-debug-master.py * Delete convert-permute-debug.py * convert-llama-hf-to-gguf.py : fix attn_q permute * gguf.py : fix rope scale kv * convert-llama-hf-to-gguf.py : rope scale and added tokens * convert-llama-7b-pth-to-gguf.py : rope scale and added tokens * llama.cpp : use rope scale kv * convert-llama-7b-pth-to-gguf.py : rope scale fix * convert-llama-hf-to-gguf.py : rope scale fix * py : fix whitespace * gguf : add Python script to convert GGMLv3 LLaMA models to GGUF (#2682) * First pass at converting GGMLv3 LLaMA models to GGUF * Cleanups, better output during conversion * Fix vocab space conversion logic * More vocab conversion fixes * Add description to converted GGUF files * Improve help text, expand warning * Allow specifying name and description for output GGUF * Allow overriding vocab and hyperparams from original model metadata * Use correct params override var name * Fix wrong type size for Q8_K Better handling of original style metadata * Set default value for gguf add_tensor raw_shape KW arg * llama : improve token type support (#2668) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * llama : add API for token type ggml-ci * tests : use new tokenizer type API (#2692) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * Improve commentary * Use token type API in test-tokenizer-1.cpp * py : cosmetics * readme : add notice about new file format ggml-ci --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> Co-authored-by: goerch <jhr.walter@t-online.de> Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
2023-08-21 20:07:43 +00:00
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:full --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
```
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
or with a light image:
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
```bash
gguf : new file format with flexible meta data (beta) (#2398) * gguf : first API pass * gguf : read header + meta data * gguf : read tensor info * gguf : initial model loading - not tested * gguf : add gguf_get_tensor_name() * gguf : do not support passing existing ggml_context to gguf_init * gguf : simplify gguf_get_val * gguf : gguf.c is now part of ggml.c * gguf : read / write sample models * gguf : add comments * refactor : reduce code duplication and better API (#2415) * gguf : expose the gguf_type enum through the API for now * gguf : add array support * gguf.py : some code style changes * convert.py : start a new simplified implementation by removing old stuff * convert.py : remove GGML vocab + other obsolete stuff * GGUF : write tensor (#2426) * WIP: Write tensor * GGUF : Support writing tensors in Python * refactor : rm unused import and upd todos * fix : fix errors upd writing example * rm example.gguf * gitignore *.gguf * undo formatting * gguf : add gguf_find_key (#2438) * gguf.cpp : find key example * ggml.h : add gguf_find_key * ggml.c : add gguf_find_key * gguf : fix writing tensors * gguf : do not hardcode tensor names to read * gguf : write sample tensors to read * gguf : add tokenization constants * quick and dirty conversion example * gguf : fix writing gguf arrays * gguf : write tensors one by one and code reuse * gguf : fix writing gguf arrays * gguf : write tensors one by one * gguf : write tensors one by one * gguf : write tokenizer data * gguf : upd gguf conversion script * Update convert-llama-h5-to-gguf.py * gguf : handle already encoded string * ggml.h : get array str and f32 * ggml.c : get arr str and f32 * gguf.py : support any type * Update convert-llama-h5-to-gguf.py * gguf : fix set is not subscriptable * gguf : update convert-llama-h5-to-gguf.py * constants.py : add layer norm eps * gguf.py : add layer norm eps and merges * ggml.h : increase GGML_MAX_NAME to 64 * ggml.c : add gguf_get_arr_n * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Makefile : add gptneox gguf example * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Update convert-llama-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * gguf : support custom alignment value * gguf : fix typo in function call * gguf : mmap tensor data example * fix : update convert-llama-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * convert-gptneox-h5-to-gguf.py : Special tokens * gptneox-main.cpp : special tokens * Update gptneox-main.cpp * constants.py : special tokens * gguf.py : accumulate kv and tensor info data + special tokens * convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens * gguf : gguf counterpart of llama-util.h * gguf-util.h : update note * convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens * convert-llama-h5-to-gguf.py : special tokens * Delete gptneox-common.cpp * Delete gptneox-common.h * convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer * gptneox-main.cpp : gpt2 bpe tokenizer * gpt2 bpe tokenizer (handles merges and unicode) * Makefile : remove gptneox-common * gguf.py : bytesarray for gpt2bpe tokenizer * cmpnct_gpt2bpe.hpp : comments * gguf.py : use custom alignment if present * gguf : minor stuff * Update gptneox-main.cpp * map tensor names * convert-gptneox-h5-to-gguf.py : map tensor names * convert-llama-h5-to-gguf.py : map tensor names * gptneox-main.cpp : map tensor names * gguf : start implementing libllama in GGUF (WIP) * gguf : start implementing libllama in GGUF (WIP) * rm binary commited by mistake * upd .gitignore * gguf : calculate n_mult * gguf : inference with 7B model working (WIP) * gguf : rm deprecated function * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : add gguf_get_kv_type * gguf : add gguf_get_kv_type * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver * gguf : rm references to old file formats * gguf : shorter name for member variable * gguf : rm redundant method * gguf : get rid of n_mult, read n_ff from file * Update gguf_tensor_map.py * Update gptneox-main.cpp * gguf : rm references to old file magics * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : quantization is working * gguf : roper closing of file * gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : no need to convert tensors twice * convert-llama-h5-to-gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : simplify nbytes * convert-llama-h5-to-gguf.py : simplify nbytes * gptneox-main.cpp : n_layer --> n_block * constants.py : n_layer --> n_block * gguf.py : n_layer --> n_block * convert-gptneox-h5-to-gguf.py : n_layer --> n_block * convert-llama-h5-to-gguf.py : n_layer --> n_block * gptneox-main.cpp : n_layer --> n_block * Update gguf_tensor_map.py * convert-gptneox-h5-to-gguf.py : load model in parts to save memory * convert-llama-h5-to-gguf.py : load model in parts to save memory * convert : write more metadata for LLaMA * convert : rm quantization version * convert-gptneox-h5-to-gguf.py : add file_type key * gptneox-main.cpp : add file_type key * fix conflicts * gguf : add todos and comments * convert-gptneox-h5-to-gguf.py : tensor name map changes * Create gguf_namemap.py : tensor name map changes * Delete gguf_tensor_map.py * gptneox-main.cpp : tensor name map changes * convert-llama-h5-to-gguf.py : fixes * gguf.py : dont add empty strings * simple : minor style changes * gguf : use UNIX line ending * Create convert-llama-7b-pth-to-gguf.py * llama : sync gguf-llama.cpp with latest llama.cpp (#2608) * llama : sync gguf-llama.cpp with latest llama.cpp * minor : indentation + assert * llama : refactor gguf_buffer and gguf_ctx_buffer * llama : minor * gitignore : add gptneox-main * llama : tokenizer fixes (#2549) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * convert : update convert-new.py with tokenizer fixes (#2614) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * llama : sync gguf-llama with llama (#2613) * llama : sync gguf-llama with llama * tests : fix build + warnings (test-tokenizer-1 still fails) * tests : fix wstring_convert * convert : fix layer names * llama : sync gguf-llama.cpp * convert : update HF converter to new tokenizer voodoo magics * llama : update tokenizer style * convert-llama-h5-to-gguf.py : add token types * constants.py : add token types * gguf.py : add token types * convert-llama-7b-pth-to-gguf.py : add token types * gguf-llama.cpp : fix n_head_kv * convert-llama-h5-to-gguf.py : add 70b gqa support * gguf.py : add tensor data layout * convert-llama-h5-to-gguf.py : add tensor data layout * convert-llama-7b-pth-to-gguf.py : add tensor data layout * gptneox-main.cpp : add tensor data layout * convert-llama-h5-to-gguf.py : clarify the reverse permute * llama : refactor model loading code (#2620) * llama : style formatting + remove helper methods * llama : fix quantization using gguf tool * llama : simplify gguf_file_saver * llama : fix method names * llama : simplify write_header() * llama : no need to pass full file loader to the file saver just gguf_ctx * llama : gguf_file_saver write I32 * llama : refactor tensor names (#2622) * gguf: update tensor names searched in quantization * gguf : define tensor names as constants * gguf : initial write API (not tested yet) * gguf : write to file API (not tested) * gguf : initial write API ready + example * gguf : fix header write * gguf : fixes + simplify example + add ggml_nbytes_pad() * gguf : minor * llama : replace gguf_file_saver with new gguf write API * gguf : streaming support when writing files * gguf : remove oboslete write methods * gguf : remove obosolete gguf_get_arr_xxx API * llama : simplify gguf_file_loader * llama : move hparams and vocab from gguf_file_loader to llama_model_loader * llama : merge gguf-util.h in llama.cpp * llama : reorder definitions in .cpp to match .h * llama : minor simplifications * llama : refactor llama_model_loader (WIP) wip : remove ggml_ctx from llama_model_loader wip : merge gguf_file_loader in llama_model_loader * llama : fix shape prints * llama : fix Windows build + fix norm_rms_eps key * llama : throw error on missing KV paris in model meta data * llama : improve printing + log meta data * llama : switch print order of meta data --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> * gguf : deduplicate (#2629) * gguf : better type names * dedup : CPU + Metal is working * ggml : fix warnings about unused results * llama.cpp : fix line feed and compiler warning * llama : fix strncpy warning + note token_to_str does not write null * llama : restore the original load/save session implementation Will migrate this to GGUF in the future * convert-llama-h5-to-gguf.py : support alt ctx param name * ggml : assert when using ggml_mul with non-F32 src1 * examples : dedup simple --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> * gguf.py : merge all files in gguf.py * convert-new.py : pick #2427 for HF 70B support * examples/gguf : no need to keep q option for quantization any more * llama.cpp : print actual model size * llama.cpp : use ggml_elements() * convert-new.py : output gguf (#2635) * convert-new.py : output gguf (WIP) * convert-new.py : add gguf key-value pairs * llama : add hparams.ctx_train + no longer print ftype * convert-new.py : minor fixes * convert-new.py : vocab-only option should work now * llama : fix tokenizer to use llama_char_to_byte * tests : add new ggml-vocab-llama.gguf * convert-new.py : tensor name mapping * convert-new.py : add map for skipping tensor serialization * convert-new.py : convert script now works * gguf.py : pick some of the refactoring from #2644 * convert-new.py : minor fixes * convert.py : update to support GGUF output * Revert "ci : disable CI temporary to not waste energy" This reverts commit 7e82d25f40386540c2c15226300ad998ecd871ea. * convert.py : n_head_kv optional and .gguf file extension * convert.py : better always have n_head_kv and default it to n_head * llama : sync with recent PRs on master * editorconfig : ignore models folder ggml-ci * ci : update ".bin" to ".gguf" extension ggml-ci * llama : fix llama_model_loader memory leak * gptneox : move as a WIP example * llama : fix lambda capture ggml-ci * ggml : fix bug in gguf_set_kv ggml-ci * common.h : .bin --> .gguf * quantize-stats.cpp : .bin --> .gguf * convert.py : fix HF tensor permuting / unpacking ggml-ci * llama.cpp : typo * llama : throw error if gguf fails to init from file ggml-ci * llama : fix tensor name grepping during quantization ggml-ci * gguf.py : write tensors in a single pass (#2644) * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : style fixes in simple conversion script * gguf : refactor gptneox conversion script * gguf : rename h5 to hf (for HuggingFace) * gguf : refactor pth to gguf conversion script * gguf : rm file_type key and method * gguf.py : fix vertical alignment * gguf.py : indentation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * convert-gptneox-hf-to-gguf.py : fixes * gguf.py : gptneox mapping * convert-llama-hf-to-gguf.py : fixes * convert-llama-7b-pth-to-gguf.py : fixes * ggml.h : reverse GGUF_MAGIC * gguf.py : reverse GGUF_MAGIC * test-tokenizer-0.cpp : fix warning * llama.cpp : print kv general.name * llama.cpp : get special token kv and linefeed token id * llama : print number of tensors per type + print arch + style * tests : update vocab file with new magic * editorconfig : fix whitespaces * llama : re-order functions * llama : remove C++ API + reorganize common source in /common dir * llama : minor API updates * llama : avoid hardcoded special tokens * llama : fix MPI build ggml-ci * llama : introduce enum llama_vocab_type + remove hardcoded string constants * convert-falcon-hf-to-gguf.py : falcon HF --> gguf conversion, not tested * falcon-main.cpp : falcon inference example * convert-falcon-hf-to-gguf.py : remove extra kv * convert-gptneox-hf-to-gguf.py : remove extra kv * convert-llama-7b-pth-to-gguf.py : remove extra kv * convert-llama-hf-to-gguf.py : remove extra kv * gguf.py : fix for falcon 40b * falcon-main.cpp : fix for falcon 40b * convert-falcon-hf-to-gguf.py : update ref * convert-falcon-hf-to-gguf.py : add tensor data layout * cmpnct_gpt2bpe.hpp : fixes * falcon-main.cpp : fixes * gptneox-main.cpp : fixes * cmpnct_gpt2bpe.hpp : remove non-general stuff * Update examples/server/README.md Co-authored-by: slaren <slarengh@gmail.com> * cmpnct_gpt2bpe.hpp : cleanup * convert-llama-hf-to-gguf.py : special tokens * convert-llama-7b-pth-to-gguf.py : special tokens * convert-permute-debug.py : permute debug print * convert-permute-debug-master.py : permute debug for master * convert-permute-debug.py : change permute type of attn_q * convert.py : 70b model working (change attn_q permute) * Delete convert-permute-debug-master.py * Delete convert-permute-debug.py * convert-llama-hf-to-gguf.py : fix attn_q permute * gguf.py : fix rope scale kv * convert-llama-hf-to-gguf.py : rope scale and added tokens * convert-llama-7b-pth-to-gguf.py : rope scale and added tokens * llama.cpp : use rope scale kv * convert-llama-7b-pth-to-gguf.py : rope scale fix * convert-llama-hf-to-gguf.py : rope scale fix * py : fix whitespace * gguf : add Python script to convert GGMLv3 LLaMA models to GGUF (#2682) * First pass at converting GGMLv3 LLaMA models to GGUF * Cleanups, better output during conversion * Fix vocab space conversion logic * More vocab conversion fixes * Add description to converted GGUF files * Improve help text, expand warning * Allow specifying name and description for output GGUF * Allow overriding vocab and hyperparams from original model metadata * Use correct params override var name * Fix wrong type size for Q8_K Better handling of original style metadata * Set default value for gguf add_tensor raw_shape KW arg * llama : improve token type support (#2668) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * llama : add API for token type ggml-ci * tests : use new tokenizer type API (#2692) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * Improve commentary * Use token type API in test-tokenizer-1.cpp * py : cosmetics * readme : add notice about new file format ggml-ci --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> Co-authored-by: goerch <jhr.walter@t-online.de> Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
2023-08-21 20:07:43 +00:00
docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512
🚀 Dockerize llamacpp (#132) * feat: dockerize llamacpp * feat: split build & runtime stages * split dockerfile into main & tools * add quantize into tool docker image * Update .devops/tools.sh Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * add docker action pipeline * change CI to publish at github docker registry * fix name runs-on macOS-latest is macos-latest (lowercase) * include docker versioned images * fix github action docker * fix docker.yml * feat: include all-in-one command tool & update readme.md --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-03-17 09:47:06 +00:00
```
Add section to README on how to run the project on Android (#130)
2023-03-14 13:30:08 +00:00
docker : add server-first container images (#5157) * feat: add Dockerfiles for each platform that user ./server instead of ./main * feat: update .github/workflows/docker.yml to build server-first docker containers * doc: add information about running the server with Docker to README.md * doc: add information about running with docker to the server README * doc: update n-gpu-layers to show correct GPU usage * fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA
2024-01-28 07:55:31 +00:00
or with a server image:
```bash
docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512
```
docker : add support for CUDA in docker (#1461) Co-authored-by: canardleteer <eris.has.a.dad+github@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-07 18:25:25 +00:00
### Docker With CUDA
Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container.
#### Building Locally
```bash
docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile .
docker build -t local/llama.cpp:light-cuda -f .devops/main-cuda.Dockerfile .
docker : add server-first container images (#5157) * feat: add Dockerfiles for each platform that user ./server instead of ./main * feat: update .github/workflows/docker.yml to build server-first docker containers * doc: add information about running the server with Docker to README.md * doc: add information about running with docker to the server README * doc: update n-gpu-layers to show correct GPU usage * fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA
2024-01-28 07:55:31 +00:00
docker build -t local/llama.cpp:server-cuda -f .devops/server-cuda.Dockerfile .
docker : add support for CUDA in docker (#1461) Co-authored-by: canardleteer <eris.has.a.dad+github@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-07 18:25:25 +00:00
```
You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture.
The defaults are:
- `CUDA_VERSION` set to `11.7.1`
- `CUDA_DOCKER_ARCH` set to `all`
The resulting images, are essentially the same as the non-CUDA images:
1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization.
2. `local/llama.cpp:light-cuda`: This image only includes the main executable file.
docker : add server-first container images (#5157) * feat: add Dockerfiles for each platform that user ./server instead of ./main * feat: update .github/workflows/docker.yml to build server-first docker containers * doc: add information about running the server with Docker to README.md * doc: add information about running with docker to the server README * doc: update n-gpu-layers to show correct GPU usage * fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA
2024-01-28 07:55:31 +00:00
3. `local/llama.cpp:server-cuda`: This image only includes the server executable file.
docker : add support for CUDA in docker (#1461) Co-authored-by: canardleteer <eris.has.a.dad+github@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-07 18:25:25 +00:00
#### Usage
After building locally, Usage is similar to the non-CUDA examples, but you'll need to add the `--gpus` flag. You will also want to use the `--n-gpu-layers` flag.
```bash
gguf : new file format with flexible meta data (beta) (#2398) * gguf : first API pass * gguf : read header + meta data * gguf : read tensor info * gguf : initial model loading - not tested * gguf : add gguf_get_tensor_name() * gguf : do not support passing existing ggml_context to gguf_init * gguf : simplify gguf_get_val * gguf : gguf.c is now part of ggml.c * gguf : read / write sample models * gguf : add comments * refactor : reduce code duplication and better API (#2415) * gguf : expose the gguf_type enum through the API for now * gguf : add array support * gguf.py : some code style changes * convert.py : start a new simplified implementation by removing old stuff * convert.py : remove GGML vocab + other obsolete stuff * GGUF : write tensor (#2426) * WIP: Write tensor * GGUF : Support writing tensors in Python * refactor : rm unused import and upd todos * fix : fix errors upd writing example * rm example.gguf * gitignore *.gguf * undo formatting * gguf : add gguf_find_key (#2438) * gguf.cpp : find key example * ggml.h : add gguf_find_key * ggml.c : add gguf_find_key * gguf : fix writing tensors * gguf : do not hardcode tensor names to read * gguf : write sample tensors to read * gguf : add tokenization constants * quick and dirty conversion example * gguf : fix writing gguf arrays * gguf : write tensors one by one and code reuse * gguf : fix writing gguf arrays * gguf : write tensors one by one * gguf : write tensors one by one * gguf : write tokenizer data * gguf : upd gguf conversion script * Update convert-llama-h5-to-gguf.py * gguf : handle already encoded string * ggml.h : get array str and f32 * ggml.c : get arr str and f32 * gguf.py : support any type * Update convert-llama-h5-to-gguf.py * gguf : fix set is not subscriptable * gguf : update convert-llama-h5-to-gguf.py * constants.py : add layer norm eps * gguf.py : add layer norm eps and merges * ggml.h : increase GGML_MAX_NAME to 64 * ggml.c : add gguf_get_arr_n * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Makefile : add gptneox gguf example * Update convert-llama-h5-to-gguf.py * add gptneox gguf example * Update convert-llama-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-gptneox-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * gguf : support custom alignment value * gguf : fix typo in function call * gguf : mmap tensor data example * fix : update convert-llama-h5-to-gguf.py * Update convert-llama-h5-to-gguf.py * convert-gptneox-h5-to-gguf.py : Special tokens * gptneox-main.cpp : special tokens * Update gptneox-main.cpp * constants.py : special tokens * gguf.py : accumulate kv and tensor info data + special tokens * convert-gptneox-h5-to-gguf.py : accumulate kv and ti + special tokens * gguf : gguf counterpart of llama-util.h * gguf-util.h : update note * convert-llama-h5-to-gguf.py : accumulate kv / ti + special tokens * convert-llama-h5-to-gguf.py : special tokens * Delete gptneox-common.cpp * Delete gptneox-common.h * convert-gptneox-h5-to-gguf.py : gpt2bpe tokenizer * gptneox-main.cpp : gpt2 bpe tokenizer * gpt2 bpe tokenizer (handles merges and unicode) * Makefile : remove gptneox-common * gguf.py : bytesarray for gpt2bpe tokenizer * cmpnct_gpt2bpe.hpp : comments * gguf.py : use custom alignment if present * gguf : minor stuff * Update gptneox-main.cpp * map tensor names * convert-gptneox-h5-to-gguf.py : map tensor names * convert-llama-h5-to-gguf.py : map tensor names * gptneox-main.cpp : map tensor names * gguf : start implementing libllama in GGUF (WIP) * gguf : start implementing libllama in GGUF (WIP) * rm binary commited by mistake * upd .gitignore * gguf : calculate n_mult * gguf : inference with 7B model working (WIP) * gguf : rm deprecated function * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : start implementing gguf_file_saver (WIP) * gguf : add gguf_get_kv_type * gguf : add gguf_get_kv_type * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver (WIP) * gguf : write metadata in gguf_file_saver * gguf : rm references to old file formats * gguf : shorter name for member variable * gguf : rm redundant method * gguf : get rid of n_mult, read n_ff from file * Update gguf_tensor_map.py * Update gptneox-main.cpp * gguf : rm references to old file magics * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : start implementing quantization (WIP) * gguf : quantization is working * gguf : roper closing of file * gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : no need to convert tensors twice * convert-llama-h5-to-gguf.py : no need to convert tensors twice * convert-gptneox-h5-to-gguf.py : simplify nbytes * convert-llama-h5-to-gguf.py : simplify nbytes * gptneox-main.cpp : n_layer --> n_block * constants.py : n_layer --> n_block * gguf.py : n_layer --> n_block * convert-gptneox-h5-to-gguf.py : n_layer --> n_block * convert-llama-h5-to-gguf.py : n_layer --> n_block * gptneox-main.cpp : n_layer --> n_block * Update gguf_tensor_map.py * convert-gptneox-h5-to-gguf.py : load model in parts to save memory * convert-llama-h5-to-gguf.py : load model in parts to save memory * convert : write more metadata for LLaMA * convert : rm quantization version * convert-gptneox-h5-to-gguf.py : add file_type key * gptneox-main.cpp : add file_type key * fix conflicts * gguf : add todos and comments * convert-gptneox-h5-to-gguf.py : tensor name map changes * Create gguf_namemap.py : tensor name map changes * Delete gguf_tensor_map.py * gptneox-main.cpp : tensor name map changes * convert-llama-h5-to-gguf.py : fixes * gguf.py : dont add empty strings * simple : minor style changes * gguf : use UNIX line ending * Create convert-llama-7b-pth-to-gguf.py * llama : sync gguf-llama.cpp with latest llama.cpp (#2608) * llama : sync gguf-llama.cpp with latest llama.cpp * minor : indentation + assert * llama : refactor gguf_buffer and gguf_ctx_buffer * llama : minor * gitignore : add gptneox-main * llama : tokenizer fixes (#2549) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * convert : update convert-new.py with tokenizer fixes (#2614) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * llama : sync gguf-llama with llama (#2613) * llama : sync gguf-llama with llama * tests : fix build + warnings (test-tokenizer-1 still fails) * tests : fix wstring_convert * convert : fix layer names * llama : sync gguf-llama.cpp * convert : update HF converter to new tokenizer voodoo magics * llama : update tokenizer style * convert-llama-h5-to-gguf.py : add token types * constants.py : add token types * gguf.py : add token types * convert-llama-7b-pth-to-gguf.py : add token types * gguf-llama.cpp : fix n_head_kv * convert-llama-h5-to-gguf.py : add 70b gqa support * gguf.py : add tensor data layout * convert-llama-h5-to-gguf.py : add tensor data layout * convert-llama-7b-pth-to-gguf.py : add tensor data layout * gptneox-main.cpp : add tensor data layout * convert-llama-h5-to-gguf.py : clarify the reverse permute * llama : refactor model loading code (#2620) * llama : style formatting + remove helper methods * llama : fix quantization using gguf tool * llama : simplify gguf_file_saver * llama : fix method names * llama : simplify write_header() * llama : no need to pass full file loader to the file saver just gguf_ctx * llama : gguf_file_saver write I32 * llama : refactor tensor names (#2622) * gguf: update tensor names searched in quantization * gguf : define tensor names as constants * gguf : initial write API (not tested yet) * gguf : write to file API (not tested) * gguf : initial write API ready + example * gguf : fix header write * gguf : fixes + simplify example + add ggml_nbytes_pad() * gguf : minor * llama : replace gguf_file_saver with new gguf write API * gguf : streaming support when writing files * gguf : remove oboslete write methods * gguf : remove obosolete gguf_get_arr_xxx API * llama : simplify gguf_file_loader * llama : move hparams and vocab from gguf_file_loader to llama_model_loader * llama : merge gguf-util.h in llama.cpp * llama : reorder definitions in .cpp to match .h * llama : minor simplifications * llama : refactor llama_model_loader (WIP) wip : remove ggml_ctx from llama_model_loader wip : merge gguf_file_loader in llama_model_loader * llama : fix shape prints * llama : fix Windows build + fix norm_rms_eps key * llama : throw error on missing KV paris in model meta data * llama : improve printing + log meta data * llama : switch print order of meta data --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> * gguf : deduplicate (#2629) * gguf : better type names * dedup : CPU + Metal is working * ggml : fix warnings about unused results * llama.cpp : fix line feed and compiler warning * llama : fix strncpy warning + note token_to_str does not write null * llama : restore the original load/save session implementation Will migrate this to GGUF in the future * convert-llama-h5-to-gguf.py : support alt ctx param name * ggml : assert when using ggml_mul with non-F32 src1 * examples : dedup simple --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> * gguf.py : merge all files in gguf.py * convert-new.py : pick #2427 for HF 70B support * examples/gguf : no need to keep q option for quantization any more * llama.cpp : print actual model size * llama.cpp : use ggml_elements() * convert-new.py : output gguf (#2635) * convert-new.py : output gguf (WIP) * convert-new.py : add gguf key-value pairs * llama : add hparams.ctx_train + no longer print ftype * convert-new.py : minor fixes * convert-new.py : vocab-only option should work now * llama : fix tokenizer to use llama_char_to_byte * tests : add new ggml-vocab-llama.gguf * convert-new.py : tensor name mapping * convert-new.py : add map for skipping tensor serialization * convert-new.py : convert script now works * gguf.py : pick some of the refactoring from #2644 * convert-new.py : minor fixes * convert.py : update to support GGUF output * Revert "ci : disable CI temporary to not waste energy" This reverts commit 7e82d25f40386540c2c15226300ad998ecd871ea. * convert.py : n_head_kv optional and .gguf file extension * convert.py : better always have n_head_kv and default it to n_head * llama : sync with recent PRs on master * editorconfig : ignore models folder ggml-ci * ci : update ".bin" to ".gguf" extension ggml-ci * llama : fix llama_model_loader memory leak * gptneox : move as a WIP example * llama : fix lambda capture ggml-ci * ggml : fix bug in gguf_set_kv ggml-ci * common.h : .bin --> .gguf * quantize-stats.cpp : .bin --> .gguf * convert.py : fix HF tensor permuting / unpacking ggml-ci * llama.cpp : typo * llama : throw error if gguf fails to init from file ggml-ci * llama : fix tensor name grepping during quantization ggml-ci * gguf.py : write tensors in a single pass (#2644) * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : single pass for writing tensors + refactoring writer * gguf : style fixes in simple conversion script * gguf : refactor gptneox conversion script * gguf : rename h5 to hf (for HuggingFace) * gguf : refactor pth to gguf conversion script * gguf : rm file_type key and method * gguf.py : fix vertical alignment * gguf.py : indentation --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * convert-gptneox-hf-to-gguf.py : fixes * gguf.py : gptneox mapping * convert-llama-hf-to-gguf.py : fixes * convert-llama-7b-pth-to-gguf.py : fixes * ggml.h : reverse GGUF_MAGIC * gguf.py : reverse GGUF_MAGIC * test-tokenizer-0.cpp : fix warning * llama.cpp : print kv general.name * llama.cpp : get special token kv and linefeed token id * llama : print number of tensors per type + print arch + style * tests : update vocab file with new magic * editorconfig : fix whitespaces * llama : re-order functions * llama : remove C++ API + reorganize common source in /common dir * llama : minor API updates * llama : avoid hardcoded special tokens * llama : fix MPI build ggml-ci * llama : introduce enum llama_vocab_type + remove hardcoded string constants * convert-falcon-hf-to-gguf.py : falcon HF --> gguf conversion, not tested * falcon-main.cpp : falcon inference example * convert-falcon-hf-to-gguf.py : remove extra kv * convert-gptneox-hf-to-gguf.py : remove extra kv * convert-llama-7b-pth-to-gguf.py : remove extra kv * convert-llama-hf-to-gguf.py : remove extra kv * gguf.py : fix for falcon 40b * falcon-main.cpp : fix for falcon 40b * convert-falcon-hf-to-gguf.py : update ref * convert-falcon-hf-to-gguf.py : add tensor data layout * cmpnct_gpt2bpe.hpp : fixes * falcon-main.cpp : fixes * gptneox-main.cpp : fixes * cmpnct_gpt2bpe.hpp : remove non-general stuff * Update examples/server/README.md Co-authored-by: slaren <slarengh@gmail.com> * cmpnct_gpt2bpe.hpp : cleanup * convert-llama-hf-to-gguf.py : special tokens * convert-llama-7b-pth-to-gguf.py : special tokens * convert-permute-debug.py : permute debug print * convert-permute-debug-master.py : permute debug for master * convert-permute-debug.py : change permute type of attn_q * convert.py : 70b model working (change attn_q permute) * Delete convert-permute-debug-master.py * Delete convert-permute-debug.py * convert-llama-hf-to-gguf.py : fix attn_q permute * gguf.py : fix rope scale kv * convert-llama-hf-to-gguf.py : rope scale and added tokens * convert-llama-7b-pth-to-gguf.py : rope scale and added tokens * llama.cpp : use rope scale kv * convert-llama-7b-pth-to-gguf.py : rope scale fix * convert-llama-hf-to-gguf.py : rope scale fix * py : fix whitespace * gguf : add Python script to convert GGMLv3 LLaMA models to GGUF (#2682) * First pass at converting GGMLv3 LLaMA models to GGUF * Cleanups, better output during conversion * Fix vocab space conversion logic * More vocab conversion fixes * Add description to converted GGUF files * Improve help text, expand warning * Allow specifying name and description for output GGUF * Allow overriding vocab and hyperparams from original model metadata * Use correct params override var name * Fix wrong type size for Q8_K Better handling of original style metadata * Set default value for gguf add_tensor raw_shape KW arg * llama : improve token type support (#2668) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * llama : add API for token type ggml-ci * tests : use new tokenizer type API (#2692) * Merge tokenizer fixes into the gguf branch. * Add test vocabularies * Adapt convert-new.py (and fix a clang-cl compiler error on windows) * Improved tokenizer test But does it work on MacOS? * Improve token type support - Added @klosax code to convert.py - Improved token type support in vocabulary * Exclude platform dependent tests * More sentencepiece compatibility by eliminating magic numbers * Restored accidentally removed comment * Improve commentary * Use token type API in test-tokenizer-1.cpp * py : cosmetics * readme : add notice about new file format ggml-ci --------- Co-authored-by: M. Yusuf Sarıgöz <yusufsarigoz@gmail.com> Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> Co-authored-by: goerch <jhr.walter@t-online.de> Co-authored-by: slaren <slarengh@gmail.com> Co-authored-by: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com>
2023-08-21 20:07:43 +00:00
docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1
docker : add server-first container images (#5157) * feat: add Dockerfiles for each platform that user ./server instead of ./main * feat: update .github/workflows/docker.yml to build server-first docker containers * doc: add information about running the server with Docker to README.md * doc: add information about running with docker to the server README * doc: update n-gpu-layers to show correct GPU usage * fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA
2024-01-28 07:55:31 +00:00
docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1
docker : add support for CUDA in docker (#1461) Co-authored-by: canardleteer <eris.has.a.dad+github@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-07-07 18:25:25 +00:00
```
Add initial contribution guidelines
2023-03-13 07:42:26 +00:00
### Contributing
Update contribution section, hot topics, limitations, etc.
2023-03-13 17:21:51 +00:00
- Contributors can open PRs
Expand "Contributing" section
2023-03-16 06:55:13 +00:00
- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
Add initial contribution guidelines
2023-03-13 07:42:26 +00:00
- Collaborators will be invited based on contributions
Expand "Contributing" section
2023-03-16 06:55:13 +00:00
- Any help with managing issues and PRs is very appreciated!
Update Contributing section
2023-03-17 18:30:04 +00:00
- Make sure to read this: [Inference at the edge](https://github.com/ggerganov/llama.cpp/discussions/205)
Adjust repetition penalty ..
2023-03-23 08:46:58 +00:00
- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)
Add initial contribution guidelines
2023-03-13 07:42:26 +00:00
Add Misc section + update hot topics + minor fixes
2023-03-14 07:43:52 +00:00
### Coding guidelines
Add initial contribution guidelines
2023-03-13 07:42:26 +00:00
- Avoid adding third-party dependencies, extra files, extra headers, etc.
- Always consider cross-compatibility with other operating systems and architectures
Add Misc section + update hot topics + minor fixes
2023-03-14 07:43:52 +00:00
- Avoid fancy looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
Add initial contribution guidelines
2023-03-13 07:42:26 +00:00
- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
Minor: Readme fixed grammar, spelling, and misc updates (#1071)
2023-04-19 19:52:14 +00:00
- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
Add Misc section + update hot topics + minor fixes
2023-03-14 07:43:52 +00:00
- See [good first issues](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
readme : update coding guidelines
2023-12-21 17:27:14 +00:00
- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
- Matrix multiplication is unconventional: [`z = ggml_mul_mat(ctx, x, y)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means `zT = x @ yT`
Move model section from issue template to README.md (#421) * Update custom.md * Removed Model section as it is better placed in README.md * Updates to README.md model section * Inserted text that was removed from issue template about obtaining models from FB and links to papers describing the various models * Removed IPF down links for the Alpaca 7B models as these look to be in the old data format and probably shouldn't be directly linked to, anyway * Updated the perplexity section to point at Perplexity scores #406 discussion
2023-03-23 11:30:40 +00:00
readme : change logo + add bindings + add uis + add wiki
2023-04-05 15:56:20 +00:00
### Docs
readme : add more docs indexes (#2127) * Update README.md to add more docs indexes * Update README.md to add more docs indexes
2023-07-09 07:38:42 +00:00
- [main](./examples/main/README.md)
- [server](./examples/server/README.md)
- [jeopardy](./examples/jeopardy/README.md)
- [BLIS](./docs/BLIS.md)
docs : add performance troubleshoot + example benchmark documentation (#1674) * test anchor link * test table * add benchmarks * Add performance troubleshoot & benchmark * add benchmarks * remove unneeded line --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
2023-06-05 20:32:36 +00:00
- [Performance troubleshooting](./docs/token_generation_performance_tips.md)
readme : add more docs indexes (#2127) * Update README.md to add more docs indexes * Update README.md to add more docs indexes
2023-07-09 07:38:42 +00:00
- [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
docs : add grammar docs (#2701) * docs : add grammar docs * tweaks to grammar guide * rework GBNF example to be a commented grammar
2023-08-23 01:01:57 +00:00
- [GBNF grammars](./grammars/README.md)
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