19fdba56b5
Add Multi-Token Prediction (MTP) speculative decoding for Qwen3.5 dense models (0.8B-27B). The MTP head uses a full transformer block (attention + FFN) to predict the next-next token, enabling ~28 tok/s on RTX 5060 Ti. Key changes: - Model loading: Qwen3.5 MTP layer tensors (nextn.eh_proj, attention weights, FFN) loaded into layers[n_layer-1] - Graph builder: Full MTP head with self-attention, gated RoPE, FFN, and vocabulary projection. Unfiltered hidden state passed for proper KV cache population during prompt processing. - FastMTP: Vocabulary trimming from 248K to 32K tokens via ggml_view_2d on the lm_head. Reduces draft generation from 22ms to 6ms (3.7x). - Speculative framework: MTP auto-detection for hybrid models, fuzzy seq_rm checkpoint matching for DeltaNet rollback. - Server: Two-phase decode option for hybrid/recurrent models to avoid DeltaNet state corruption from rejected drafts. - Recurrent state: Fixed copy_cell (ggml_view_1d takes element count, not bytes), buffer assignment for no_alloc views. Results on Qwen3.5-9B Q4_K_M (RTX 5060 Ti 16GB): - 28.1 tok/s with 82% acceptance rate (temp=0) - 92% acceptance with two-phase decode (correct output, 15 tok/s) - Draft generation: 6.1ms with FastMTP (vs 22.4ms full vocab) |
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| examples | ||
| gguf | ||
| 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
Optionally, you can install gguf with the extra 'gui' to enable the visual GGUF editor.
pip install gguf[gui]
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.
examples/reader.py — Extracts and displays key-value pairs and tensor details from a GGUF file in a readable format.
gguf/scripts/gguf_dump.py — Dumps a GGUF file's metadata to the console.
gguf/scripts/gguf_set_metadata.py — Allows changing simple metadata values in a GGUF file by key.
gguf/scripts/gguf_convert_endian.py — Allows converting the endianness of GGUF files.
gguf/scripts/gguf_new_metadata.py — Copies a GGUF file with added/modified/removed metadata values.
gguf/scripts/gguf_editor_gui.py — Allows for viewing, editing, adding, or removing metadata values within a GGUF file as well as viewing its tensors with a Qt interface.
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.