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Initial release
2023-03-10 18:40:58 +00:00
*.o
*.a
ggml : use 8-bit precision for Q4_1 intermediate results (#1047) * ggml : use 8-bit precision for Q4_1 intermediate results (ARM) * ggml : optimize ggml_vec_dot_q4_1_q8_0() via vmalq_n_f32 56 ms/token with Q4_1 ! * ggml : AVX2 implementation of ggml_vec_dot_q4_1_q8_0 (#1051) * gitignore : ignore ppl-*.txt files --------- Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
2023-04-19 17:10:08 +00:00
.DS_Store
.build/
Initial release
2023-03-10 18:40:58 +00:00
.cache/
ggml : use 8-bit precision for Q4_1 intermediate results (#1047) * ggml : use 8-bit precision for Q4_1 intermediate results (ARM) * ggml : optimize ggml_vec_dot_q4_1_q8_0() via vmalq_n_f32 56 ms/token with Q4_1 ! * ggml : AVX2 implementation of ggml_vec_dot_q4_1_q8_0 (#1051) * gitignore : ignore ppl-*.txt files --------- Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
2023-04-19 17:10:08 +00:00
.direnv/
.envrc
.swiftpm
.venv
Initial release
2023-03-10 18:40:58 +00:00
.vs/
.vscode/
build/
build-em/
build-debug/
build-release/
build-static/
ggml : add Q5_0 and Q5_1 quantization (#1187) * ggml : add Q5_0 quantization (cuBLAS only) * ggml : fix Q5_0 qh -> uint32_t * ggml : fix q5_0 histogram stats * ggml : q5_0 scalar dot product * ggml : q5_0 ARM NEON dot * ggml : q5_0 more efficient ARM NEON using uint64_t masks * ggml : rename Q5_0 -> Q5_1 * ggml : adding Q5_0 mode * quantize : add Q5_0 and Q5_1 to map * ggml : AVX2 optimizations for Q5_0, Q5_1 (#1195) --------- Co-authored-by: Stephan Walter <stephan@walter.name>
2023-04-26 20:14:13 +00:00
build-cublas/
Initial release
2023-03-10 18:40:58 +00:00
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/
.gitignore models/
2023-03-11 10:26:46 +00:00
models/*
Initial release
2023-03-10 18:40:58 +00:00
/main
/quantize
Add quantize-stats command for testing quantization (#728) Command that calculates some statistics over the errors introduced by quantization, like mean square error, max error and some percentile errors for layer weights. Should be useful for testing quantization improvements. Exposes some internal state from ggml and llama for testing
2023-04-07 22:09:18 +00:00
/quantize-stats
Nix flake (#40) * Nix flake * Nix: only add Accelerate framework on macOS * Nix: development shel, direnv and compatibility * Nix: use python packages supplied by withPackages * Nix: remove channel compatibility * Nix: fix ARM neon dotproduct on macOS --------- Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
2023-03-17 22:03:48 +00:00
/result
Overhaul the examples structure - main -> examples - utils -> examples (renamed to "common") - quantize -> examples - separate tools for "perplexity" and "embedding" Hope I didn't break something !
2023-03-25 18:26:40 +00:00
/perplexity
gitignore : add "embedding"
2023-03-28 15:34:35 +00:00
/embedding
gitignore : benchmark
2023-04-13 15:01:22 +00:00
/benchmark-q4_0-matmult
gitignore : vdot
2023-04-18 20:00:08 +00:00
/vdot
Make loading weights 10-100x faster This is a breaking change that's going to give you three benefits: 1. Your inference commands should load 100x faster 2. You may be able to safely load models 2x larger 3. You can run many concurrent inference processes This was accomplished by changing the file format so we can mmap() weights directly into memory without having to read() or copy them thereby ensuring the kernel can make its file cache pages directly accessible to our inference processes; and secondly, that the file cache pages are much less likely to get evicted (which would force loads to hit disk) because they're no longer competing with memory pages that were needlessly created by gigabytes of standard i/o. The new file format supports single-file models like LLaMA 7b, and it also supports multi-file models like LLaMA 13B. Our Python tool now merges the foo.1, foo.2, etc. files back into a single file so that the C++ code which maps it doesn't need to reshape data every time. That's made llama.cpp so much simpler. Much of its load code has now been deleted. Furthermore, this change ensures that tensors are aligned properly on a 32-byte boundary. That opens the door to seeing if we can get additional performance gains on some microprocessors, by using ops that require memory alignment. Lastly note that both POSIX and the Windows platform are supported Fixes #91
2023-03-29 20:51:37 +00:00
/Pipfile
Initial release
2023-03-10 18:40:58 +00:00
arm_neon.h
compile_commands.json
Nix flake (#40) * Nix flake * Nix: only add Accelerate framework on macOS * Nix: development shel, direnv and compatibility * Nix: use python packages supplied by withPackages * Nix: remove channel compatibility * Nix: fix ARM neon dotproduct on macOS --------- Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
2023-03-17 22:03:48 +00:00
deploy : add a Package.swift for SwiftPM support (#393) * Add a Package.swift for SwiftPM support * Swap from exclusions to allowlist
2023-03-28 16:39:01 +00:00
__pycache__
zig : add build.zig (#773) Co-authored-by: Locria Cyber <74560659+locriacyber@users.noreply.github.com>
2023-04-05 15:06:02 +00:00
zig-out/
zig-cache/
ggml : use 8-bit precision for Q4_1 intermediate results (#1047) * ggml : use 8-bit precision for Q4_1 intermediate results (ARM) * ggml : optimize ggml_vec_dot_q4_1_q8_0() via vmalq_n_f32 56 ms/token with Q4_1 ! * ggml : AVX2 implementation of ggml_vec_dot_q4_1_q8_0 (#1051) * gitignore : ignore ppl-*.txt files --------- Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
2023-04-19 17:10:08 +00:00
ppl-*.txt
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