|
--- |
|
license: other |
|
--- |
|
|
|
# OpenAssistant LLaMa 30B SFT 6 |
|
|
|
Due to the license attached to LLaMa models by Meta AI it is not possible to directly distribute LLaMa-based models. Instead we provide XOR weights for the OA models. |
|
|
|
Thanks to Mick for writing the `xor_codec.py` script which enables this process |
|
|
|
## The Process |
|
|
|
Note: This process applies to `oasst-sft-6-llama-30b` model. The same process can be applied to other models in future, but the checksums will be different.. |
|
|
|
To use OpenAssistant LLaMa-Based Models, you need to have a copy of the original LLaMa model weights and add them to a `llama` subdirectory here. |
|
|
|
Ensure your LLaMa 30B checkpoint matches the correct md5sums: |
|
|
|
``` |
|
f856e9d99c30855d6ead4d00cc3a5573 consolidated.00.pth |
|
d9dbfbea61309dc1e087f5081e98331a consolidated.01.pth |
|
2b2bed47912ceb828c0a37aac4b99073 consolidated.02.pth |
|
ea0405cdb5bc638fee12de614f729ebc consolidated.03.pth |
|
4babdbd05b8923226a9e9622492054b6 params.json |
|
``` |
|
|
|
**Important: Follow these exact steps to convert your original LLaMa checkpoint to a HuggingFace Transformers-compatible format. If you use the wrong versions of any dependency, you risk ending up with weights which are not compatible with the XOR files.** |
|
|
|
1. Create a clean Python **3.10** virtual environment & activate it: |
|
|
|
``` |
|
python3.10 -m venv xor_venv |
|
source xor_venv/bin/activate |
|
``` |
|
|
|
2. Clone transformers repo and switch to tested version: |
|
|
|
``` |
|
git clone https://github.com/huggingface/transformers.git |
|
cd transformers |
|
git checkout d04ec99bec8a0b432fc03ed60cea9a1a20ebaf3c |
|
pip install . |
|
``` |
|
|
|
3. Install **exactly** these dependency versions: |
|
|
|
``` |
|
pip install torch==1.13.1 accelerate==0.18.0 sentencepiece==0.1.98 protobuf==3.20.1 |
|
``` |
|
|
|
4. Check `pip freeze` output: |
|
|
|
``` |
|
accelerate==0.18.0 |
|
certifi==2022.12.7 |
|
charset-normalizer==3.1.0 |
|
filelock==3.12.0 |
|
huggingface-hub==0.13.4 |
|
idna==3.4 |
|
numpy==1.24.2 |
|
nvidia-cublas-cu11==11.10.3.66 |
|
nvidia-cuda-nvrtc-cu11==11.7.99 |
|
nvidia-cuda-runtime-cu11==11.7.99 |
|
nvidia-cudnn-cu11==8.5.0.96 |
|
packaging==23.1 |
|
protobuf==3.20.1 |
|
psutil==5.9.5 |
|
PyYAML==6.0 |
|
regex==2023.3.23 |
|
requests==2.28.2 |
|
sentencepiece==0.1.98 |
|
tokenizers==0.13.3 |
|
torch==1.13.1 |
|
tqdm==4.65.0 |
|
transformers @ file:///mnt/data/koepf/transformers |
|
typing_extensions==4.5.0 |
|
urllib3==1.26.15 |
|
``` |
|
|
|
5. While in `transformers` repo root, run HF LLaMA conversion script: |
|
|
|
``` |
|
python src/transformers/models/llama/convert_llama_weights_to_hf.py --input_dir <input_path_llama_base> --output_dir <output_path_llama30b_hf> --model_size 30B |
|
``` |
|
|
|
6. Run `find . -type f -exec md5sum "{}" +` in the conversion target directory (`output_dir`). This should produce exactly the following checksums if your files are correct: |
|
|
|
``` |
|
462a2d07f65776f27c0facfa2affb9f9 ./pytorch_model-00007-of-00007.bin |
|
e1dc8c48a65279fb1fbccff14562e6a3 ./pytorch_model-00003-of-00007.bin |
|
9cffb1aeba11b16da84b56abb773d099 ./pytorch_model-00001-of-00007.bin |
|
aee09e21813368c49baaece120125ae3 ./generation_config.json |
|
92754d6c6f291819ffc3dfcaf470f541 ./pytorch_model-00005-of-00007.bin |
|
3eddc6fc02c0172d38727e5826181adb ./pytorch_model-00004-of-00007.bin |
|
eeec4125e9c7560836b4873b6f8e3025 ./tokenizer.model |
|
99762d59efa6b96599e863893cf2da02 ./pytorch_model-00006-of-00007.bin |
|
598538f18fed1877b41f77de034c0c8a ./config.json |
|
fdb311c39b8659a5d5c1991339bafc09 ./tokenizer.json |
|
fecfda4fba7bfd911e187a85db5fa2ef ./pytorch_model.bin.index.json |
|
edd1a5897748864768b1fab645b31491 ./tokenizer_config.json |
|
6b2e0a735969660e720c27061ef3f3d3 ./special_tokens_map.json |
|
5cfcb78b908ffa02e681cce69dbe4303 ./pytorch_model-00002-of-00007.bin |
|
``` |
|
|
|
**Important: You should now have the correct LLaMa weights and be ready to apply the XORs. If the checksums above do not match yours, there is a problem.** |
|
|
|
7. Once you have LLaMa weights in the correct format, you can apply the XOR decoding: |
|
|
|
``` |
|
python xor_codec.py oasst-sft-6-llama-30b/ oasst-sft-6-llama-30b-xor/ llama30b_hf/ |
|
``` |
|
|
|
You should **expect to see one warning message** during execution: |
|
|
|
`Exception when processing 'added_tokens.json'` |
|
|
|
This is normal. **If similar messages appear for other files, something has gone wrong**. |
|
|
|
8. Now run `find . -type f -exec md5sum "{}" +` in the output directory (here `oasst-sft-6-llama-30b`). You should get a file with exactly these checksums: |
|
|
|
``` |
|
970e99665d66ba3fad6fdf9b4910acc5 ./pytorch_model-00007-of-00007.bin |
|
659fcb7598dcd22e7d008189ecb2bb42 ./pytorch_model-00003-of-00007.bin |
|
ff6e4cf43ddf02fb5d3960f850af1220 ./pytorch_model-00001-of-00007.bin |
|
27b0dc092f99aa2efaf467b2d8026c3f ./added_tokens.json |
|
2917a1cafb895cf57e746cfd7696bfe5 ./generation_config.json |
|
740c324ae65b1ec25976643cda79e479 ./pytorch_model-00005-of-00007.bin |
|
f7aefb4c63be2ac512fd905b45295235 ./pytorch_model-00004-of-00007.bin |
|
eeec4125e9c7560836b4873b6f8e3025 ./tokenizer.model |
|
369df2f0e38bda0d9629a12a77c10dfc ./pytorch_model-00006-of-00007.bin |
|
cc9dbf56b68b68a585cc7367696e06a7 ./config.json |
|
76d47e4f51a8df1d703c6f594981fcab ./pytorch_model.bin.index.json |
|
fd9452959d711be29ccf04a97598e8d1 ./tokenizer_config.json |
|
785905630a0fe583122a8446a5abe287 ./special_tokens_map.json |
|
ae48c4c68e4e171d502dd0896aa19a84 ./pytorch_model-00002-of-00007.bin |
|
``` |
|
|
|
If so you have successfully decoded the weights and should be able to use the model with HuggingFace Transformers. **If your checksums do not match those above, there is a problem.** |
|
|
