this fixes the error I encountered when trying to run the convert.py
script in a venv:
```
$ nix develop
[...]$ source .venv/bin/activate
(.venv)
[...]$ pip3 install -r requirements.txt
<... clipped ...>
[...]$ python3 ./convert.py
Traceback (most recent call last):
File "/home/mhueschen/projects-reference/llama.cpp/./convert.py", line 40, in <module>
from sentencepiece import SentencePieceProcessor
File "/home/mhueschen/projects-reference/llama.cpp/.venv/lib/python3.11/site-packages/sentencepiece/__init__.py", line 13, in <module>
from . import _sentencepiece
ImportError: libstdc++.so.6: cannot open shared object file: No such file or directory
```
however, I am not sure this is the cleanest way to address this linker
issue...
* llama : support StableLM 2 1.6B
* convert : fix Qwen's set_vocab wrongly naming all special tokens [PAD{id}]
* convert : refactor Qwen's set_vocab to use it for StableLM 2 too
* nix : add tiktoken to llama-python-extra
* convert : use presence of tokenizer.json to determine StableLM tokenizer loader
It's a less arbitrary heuristic than the vocab size.
* python: add check-requirements.sh and GitHub workflow
This script and workflow forces package versions to remain compatible
across all convert*.py scripts, while allowing secondary convert scripts
to import dependencies not wanted in convert.py.
* Move requirements into ./requirements
* Fail on "==" being used for package requirements (but can be suppressed)
* Enforce "compatible release" syntax instead of ==
* Update workflow
* Add upper version bound for transformers and protobuf
* improve check-requirements.sh
* small syntax change
* don't remove venvs if nocleanup is passed
* See if this fixes docker workflow
* Move check-requirements.sh into ./scripts/
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
* flake.lock: update to hotfix CUDA::cuda_driver
Required to support https://github.com/ggerganov/llama.cpp/pull/4606
* flake.nix: rewrite
1. Split into separate files per output.
2. Added overlays, so that this flake can be integrated into others.
The names in the overlay are `llama-cpp`, `llama-cpp-opencl`,
`llama-cpp-cuda`, and `llama-cpp-rocm` so that they fit into the
broader set of Nix packages from [nixpkgs](https://github.com/nixos/nixpkgs).
3. Use [callPackage](https://summer.nixos.org/blog/callpackage-a-tool-for-the-lazy/)
rather than `with pkgs;` so that there's dependency injection rather
than dependency lookup.
4. Add a description and meta information for each package.
The description includes a bit about what's trying to accelerate each one.
5. Use specific CUDA packages instead of cudatoolkit on the advice of SomeoneSerge.
6. Format with `serokell/nixfmt` for a consistent style.
7. Update `flake.lock` with the latest goods.
* flake.nix: use finalPackage instead of passing it manually
* nix: unclutter darwin support
* nix: pass most darwin frameworks unconditionally
...for simplicity
* *.nix: nixfmt
nix shell github:piegamesde/nixfmt/rfc101-style --command \
nixfmt flake.nix .devops/nix/*.nix
* flake.nix: add maintainers
* nix: move meta down to follow Nixpkgs style more closely
* nix: add missing meta attributes
nix: clarify the interpretation of meta.maintainers
nix: clarify the meaning of "broken" and "badPlatforms"
nix: passthru: expose the use* flags for inspection
E.g.:
```
❯ nix eval .#cuda.useCuda
true
```
* flake.nix: avoid re-evaluating nixpkgs too many times
* flake.nix: use flake-parts
* nix: migrate to pname+version
* flake.nix: overlay: expose both the namespace and the default attribute
* ci: add the (Nix) flakestry workflow
* nix: cmakeFlags: explicit OFF bools
* nix: cuda: reduce runtime closure
* nix: fewer rebuilds
* nix: respect config.cudaCapabilities
* nix: add the impure driver's location to the DT_RUNPATHs
* nix: clean sources more thoroughly
...this way outPaths change less frequently,
and so there are fewer rebuilds
* nix: explicit mpi support
* nix: explicit jetson support
* flake.nix: darwin: only expose the default
---------
Co-authored-by: Someone Serge <sergei.kozlukov@aalto.fi>
* 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>
instead of `int` (while `int` option still being supported)
This allows the following usage:
`./quantize ggml-model-f16.bin ggml-model-q4_0.bin q4_0`
instead of:
`./quantize ggml-model-f16.bin ggml-model-q4_0.bin 2`
By using `pip install torch --index-url https://download.pytorch.org/whl/cpu`
instead of `pip install torch` we can specify we want to install a CPU-only version
of PyTorch without any GPU dependencies. This reduces the size of the Docker image
from 7.32 GB to 1.62 GB
The readme tells people to use the command line option "-t 8", causing 8
threads to be started. On systems with fewer than 8 cores, this causes a
significant slowdown. Remove the option from the example command lines
and use /proc/cpuinfo on Linux to determine a sensible default.
