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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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* chore: add nodejs binding
* chore: add nodejs binding
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* update
* update readme
* minimize the changes.
---------
Co-authored-by: zjli2019 <zhengji.li@ingchips.com>
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This is to emphasize that these do not need to be obtained from elsewhere.
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* README: Update with CMake and windows example
* README: update with code-review for cmake build
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`migrate-ggml-2023-03-30-pr613.py` is needed to get gpt4all running.
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Added explicit **bolded** instructions clarifying that people need to request access to models from Facebook and never through through this repo.
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* Revert "Delete SHA256SUMS for now (#416)"
This reverts commit 8eea5ae0e5f31238a97c79ea9103c27647380e37.
* Remove ggml files until they can be verified
* Remove alpaca json
* Add also model/tokenizer.model to SHA256SUMS + update README
---------
Co-authored-by: Pavol Rusnak <pavol@rusnak.io>
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* 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
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downloads
Hashes created using:
sha256sum models/*B/*.pth models/*[7136]B/ggml-model-f16.bin* models/*[7136]B/ggml-model-q4_0.bin* > SHA256SUMS
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* potential out of bounds read
* fix quantize
* style
* Update convert-pth-to-ggml.py
* mild cleanup
* don't need the space-prefixing here rn since main.cpp already does it
* new file magic + version header field
* readme notice
* missing newlines
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
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* Improved quantize script
I improved the quantize script by adding error handling and allowing to select many models for quantization at once in the command line. I also converted it to Python for generalization as well as extensibility.
* Fixes and improvements based on Matt's observations
Fixed and improved many things in the script based on the reviews made by @mattsta. The parallelization suggestion is still to be revised, but code for it was still added (commented).
* Small fixes to the previous commit
* Corrected to use the original glob pattern
The original Bash script uses a glob pattern to match files that have endings such as ...bin.0, ...bin.1, etc. That has been translated correctly to Python now.
* Added support for Windows and updated README to use this script
New code to set the name of the quantize script binary depending on the platform has been added (quantize.exe if working on Windows) and the README.md file has been updated to use this script instead of the Bash one.
* Fixed a typo and removed shell=True in the subprocess.run call
Fixed a typo regarding the new filenames of the quantized models and removed the shell=True parameter in the subprocess.run call as it was conflicting with the list of parameters.
* Corrected previous commit
* Small tweak: changed the name of the program in argparse
This was making the automatic help message to be suggesting the program's usage as being literally "$ Quantization Script [arguments]". It should now be something like "$ python3 quantize.py [arguments]".
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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.
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