--- 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.. **This process is tested only on Linux (specifically Ubuntu). Some users have reported that the process does not work on Windows. We recommend using WSL if you only have a Windows machine.** 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 --output_dir --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/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.** ### Configuration ``` llama-30b-sft-6: dtype: fp16 log_dir: "llama_log_30b" learning_rate: 1e-5 model_name: /home/ubuntu/Open-Assistant/model/model_training/.saved/llama-30b-super-pretrain/checkpoint-3500 output_dir: llama_model_30b deepspeed_config: configs/zero3_config_sft.json weight_decay: 0.0 residual_dropout: 0.0 max_length: 2048 use_flash_attention: true warmup_steps: 20 gradient_checkpointing: true gradient_accumulation_steps: 16 per_device_train_batch_size: 2 per_device_eval_batch_size: 3 eval_steps: 101 save_steps: 292 num_train_epochs: 8 save_total_limit: 3 use_custom_sampler: true sort_by_length: false save_strategy: steps datasets: - oasst_export: lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" input_file_path: 2023-04-12_oasst_release_ready_synth.jsonl.gz val_split: 0.05 - vicuna: val_split: 0.05 max_val_set: 800 fraction: 0.8 - dolly15k: val_split: 0.05 max_val_set: 300 - grade_school_math_instructions: val_split: 0.05 - code_alpaca: val_split: 0.05 max_val_set: 250 ``` - **OASST dataset paper:** https://arxiv.org/abs/2304.07327