7945404458
* initial map to load deepseek 4 arch * wip * wip: match graph build and attn logic for dpv4 * wip: Enhance DeepSeek-V4 architecture with new tensor types and sqrtsoftplus gating function * Update DeepSeek-V4 to support raw key indexing with read/write indices * fix mismatch in attn_raw * Enable FA with CSA/HCA * Fix logit mismatch with FA path * Clean traces and logs for debug * Refactor DSV4 tensor handling for MTP execution and improve raw context management * Refactor DeepSeek4 tensor operations: replace manual weighted sum and post-processing with new helper functions * Share mHC pre-projection and fix packed DSV4 writes * DSV4: add shared top-k selection and improve mask handling * Fix DSV4 c2048 view stride and duplicate loader instantiation * Reuse shared RMS normalization in DSV4 graph * Replace DSV4 indexer rotation with shared Hadamard * Share CSA visibility mask with DSV4 LID * dsv4: document dependency ordering and reset state * Remove DSV4 zero-dependency graph shim * Fix DSV4 packed stream execution * Remove DSV4 l_out backend override * Enable DSV4 quantized K-only cache * Revert "Enable DSV4 quantized K-only cache" This reverts commit 04f9b425321f62ba60e16d1bea2f8de714cfe855. * Fix DSV4 quantized cache accounting * Fail closed on unsupported DSV4 cache lifecycle operations * Various optimizations * llama: fix GGML_METAL=ON build - missing ggml-metal.h include in llama-dflash.cpp (#2134) llama-dflash.cpp calls ggml_backend_is_metal() and ggml_backend_metal_set_n_cb() inside an #ifdef GGML_USE_METAL block but never includes ggml-metal.h, so any Metal-enabled build fails to compile. Add the same guarded include llama.cpp already uses. * New op: ggml_sum_rows_ext (#2132) * Add ggml_sum_rows_ext * openPangu: use ggml_sum_rows_ext also in mhc_post * openPangu: use ggml_sum_rows_ext also in mhc_tail * Minor * Reuse shared inverse RoPE operation for DSV4 * Reuse maintainer CUDA concat implementation * WIP * hc_pre * hc_post * Remove unnecessary mask manipulations * WIP * Take into account swiglu limits * Turn on fused indexer by default * Give names to mat mul results * More named ops * dsv4: do not uselessly copy the KV cache +20% TG at 32k tokens * mask_to_index and make CPU FA work with that * Much better CPU-only, CUDA still not functional * Better CPU TG I'm now at 9.7 t/s for zero context and 6.5 t/s for context of 32k. PP is 120 t/s for short context and 101 t/s at 32k. * Even better CPU TG I'm now at 8.1 t/s for context of 32k tokens. * Turn off DSA on CUDA for now * Fix CUDA DSA * Remove again the unnecessary softmax result buffer * Experiments * Various * More named ops * Forgot to uncomment --------- Co-authored-by: samuel <samueloliveira32df@gmail.com> Co-authored-by: hchengit <95317477+hchengit@users.noreply.github.com> |
||
|---|---|---|
| .. | ||
| examples | ||
| gguf | ||
| scripts | ||
| tests | ||
| LICENSE | ||
| README.md | ||
| pyproject.toml | ||
README.md
gguf
This is a Python package for writing binary files in the GGUF (GGML Universal File) format.
See convert_hf_to_gguf.py as an example for its usage.
Installation
pip install gguf
API Examples/Simple Tools
examples/writer.py — Generates example.gguf in the current directory to demonstrate generating a GGUF file. Note that this file cannot be used as a model.
scripts/gguf_dump.py — Dumps a GGUF file's metadata to the console.
scripts/gguf_set_metadata.py — Allows changing simple metadata values in a GGUF file by key.
scripts/gguf_convert_endian.py — Allows converting the endianness of GGUF files.
scripts/gguf_new_metadata.py — Copies a GGUF file with added/modified/removed metadata values.
Development
Maintainers who participate in development of this package are advised to install it in editable mode:
cd /path/to/llama.cpp/gguf-py
pip install --editable .
Note: This may require to upgrade your Pip installation, with a message saying that editable installation currently requires setup.py.
In this case, upgrade Pip to the latest:
pip install --upgrade pip
Automatic publishing with CI
There's a GitHub workflow to make a release automatically upon creation of tags in a specified format.
- Bump the version in
pyproject.toml. - Create a tag named
gguf-vx.x.xwherex.x.xis the semantic version number.
git tag -a gguf-v1.0.0 -m "Version 1.0 release"
- Push the tags.
git push origin --tags
Manual publishing
If you want to publish the package manually for any reason, you need to have twine and build installed:
pip install build twine
Then, follow these steps to release a new version:
- Bump the version in
pyproject.toml. - Build the package:
python -m build
- Upload the generated distribution archives:
python -m twine upload dist/*
Run Unit Tests
From root of this repository you can run this command to run all the unit tests
python -m unittest discover ./gguf-py -v
TODO
- Include conversion scripts as command line entry points in this package.