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[23/06/03] Now we support quantized training and inference (aka QLoRA). Try --quantization_bit 4/8 argument to work with quantized model. (experimental feature)
[23/05/31] Now we support training the BLOOM & BLOOMZ models in this repo. Try --model_name_or_path bigscience/bloomz-7b1-mt argument to use the BLOOMZ model.
- For pre-training:
- For supervised fine-tuning:
- For reward model training:
Please refer to data/README.md for details.
Some datasets require confirmation before using them, so we recommend logging in with your HuggingFace account using these commands.
pip install --upgrade huggingface_hub
huggingface-cli login- Python 3.8+ and PyTorch 1.13.1+
- 🤗Transformers, Datasets, Accelerate, PEFT and TRL
- protobuf, cpm_kernels and sentencepiece
- jieba, rouge_chinese and nltk (used at evaluation)
- gradio and mdtex2html (used in web_demo.py)
And powerful GPUs!
Please refer to data/example_dataset for checking the details about the format of dataset files. You can either use a single .json file or a dataset loading script with multiple files to create a custom dataset.
Note: please update data/dataset_info.json to use your custom dataset. About the format of this file, please refer to data/README.md.
git clone https://github.com/hiyouga/LLaMA-Efficient-Tuning.git
conda create -n llama_etuning python=3.10
conda activate llama_etuning
cd LLaMA-Efficient-Tuning
pip install -r requirements.txt- Download the weights of the LLaMA models.
- Convert them to HF format using the following command.
python -m transformers.models.llama.convert_llama_weights_to_hf \
--input_dir path_to_llama_weights --model_size 7B --output_dir path_to_llama_modelCUDA_VISIBLE_DEVICES=0 python src/train_pt.py \
--model_name_or_path path_to_llama_model \
--do_train \
--dataset wiki_demo \
--finetuning_type lora \
--output_dir path_to_pt_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--fp16CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
--model_name_or_path path_to_llama_model \
--do_train \
--dataset alpaca_gpt4_en \
--finetuning_type lora \
--checkpoint_dir path_to_pt_checkpoint \
--output_dir path_to_sft_checkpoint \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--resume_lora_training False \
--plot_loss \
--fp16CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
--model_name_or_path /xx/model/model_weights/Ziya-LLaMA-13B \
--do_train \
--dataset xx \
--finetuning_type lora \
--output_dir /xx/output \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-3 \
--num_train_epochs 10.0 \
--resume_lora_training False \
--plot_loss \
--fp16 \
--quantization_bit 4CUDA_VISIBLE_DEVICES=0 python src/train_rm.py \
--model_name_or_path path_to_llama_model \
--do_train \
--dataset comparison_gpt4_en \
--finetuning_type lora \
--checkpoint_dir path_to_pt_checkpoint \
--output_dir path_to_rm_checkpoint \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--plot_loss \
--fp16CUDA_VISIBLE_DEVICES=0 python src/train_ppo.py \
--model_name_or_path path_to_llama_model \
--do_train \
--dataset alpaca_gpt4_en \
--finetuning_type lora \
--checkpoint_dir path_to_pt_checkpoint,path_to_sft_checkpoint \
--reward_model path_to_rm_checkpoint \
--output_dir path_to_ppo_checkpoint \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 10 \
--save_steps 1000 \
--learning_rate 1e-5 \
--num_train_epochs 1.0 \
--resume_lora_training False \
--plot_lossaccelerate config # configure the environment
accelerate launch src/train_XX.py # arguments (same as above)CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
--model_name_or_path path_to_llama_model \
--do_eval \
--dataset alpaca_gpt4_en \
--checkpoint_dir path_to_checkpoint \
--output_dir path_to_eval_result \
--per_device_eval_batch_size 8 \
--max_samples 50 \
--predict_with_generateWe recommend using --per_device_eval_batch_size=1 and --max_target_length 128 in INT8 evaluation.
python src/cli_demo.py \
--model_name_or_path path_to_llama_model \
--checkpoint_dir path_to_checkpointpython src/web_demo.py \
--model_name_or_path path_to_llama_model \
--checkpoint_dir path_to_checkpointpython src/export_model.py \
--model_name_or_path path_to_llama_model \
--checkpoint_dir path_to_checkpoint \
--output_dir path_to_exportThis repository is licensed under the Apache-2.0 License.
Please follow the Model Card to use the LLaMA models.
Please follow the RAIL License to use the BLOOM & BLOOMZ models.
If this work is helpful, please cite as:
@Misc{llama-efficient-tuning,
title = {LLaMA Efficient Tuning},
author = {hiyouga},
howpublished = {\url{https://github.com/hiyouga/LLaMA-Efficient-Tuning}},
year = {2023}
}This repo is a sibling of ChatGLM-Efficient-Tuning. They share a similar code structure of efficient tuning on large language models.