👋 Join our WeChat.
[23/07/11] Now we support training the Baichuan-13B model in this repo. Try --model_name_or_path baichuan-inc/Baichuan-13B-Base, --padding_side right and --lora_target W_pack arguments to train the Baichuan-13B model. Remember to use --prompt_template baichuan argument when you are using the Baichuan-13B-Chat model.
[23/07/09] Now we release FastEdit⚡🩹, an easy-to-use package for editing the factual knowledge of large language models efficiently. Please follow FastEdit if you are interested.
[23/07/07] Now we support training the InternLM-7B model in this repo. Try --model_name_or_path internlm/internlm-7b argument to use the InternLM model. Remember to use --prompt_template intern argument when you are using the InternLM-chat model.
[23/07/05] Now we support training the Falcon-7B/40B models in this repo. Try --model_name_or_path tiiuae/falcon-7b and --lora_target query_key_value arguments to use the Falcon model.
[23/06/29] We provide a reproducible example of training a chat model using instruction-following datasets, see this HuggingFace Repo for details.
[23/06/22] Now we align the demo API with the OpenAI's format where you can insert the fine-tuned model in arbitrary ChatGPT-based applications.
[23/06/15] Now we support training the Baichuan-7B model in this repo. Try --model_name_or_path baichuan-inc/Baichuan-7B and --lora_target W_pack arguments to use the Baichuan-7B model. If you want to train with RTX3090, use git checkout baichuan-7b-rtx3090 to switch to the baichuan-7b-rtx3090 branch and try the --baichuan_rtx_gpu true argument. (Other RTX series GPUs can also be tried)
[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 and --lora_target query_key_value arguments to use the BLOOMZ model.
- LLaMA (7B/13B/33B/65B)
- BLOOM & BLOOMZ (560M/1.1B/1.7B/3B/7.1B/176B)
- Falcon (7B/40B)
- Baichuan (7B/13B)
- InternLM (7B)
- For pre-training:
- For supervised fine-tuning:
- Stanford Alpaca
- Stanford Alpaca (Chinese)
- GPT-4 Generated Data
- BELLE 2M
- BELLE 1M
- BELLE 0.5M
- BELLE Dialogue 0.4M
- BELLE School Math 0.25M
- BELLE Multiturn Chat 0.8M
- Guanaco Dataset
- Firefly 1.1M
- CodeAlpaca 20k
- Alpaca CoT
- Web QA (Chinese)
- UltraChat
- Open Assistant
- Open Assistant (Chinese)
- WebNovel (Chinese)
- 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
- jieba, rouge-chinese and nltk (used at evaluation)
- gradio and matplotlib (used in web_demo.py)
- uvicorn, fastapi and sse-starlette (used in api_demo.py)
And powerful GPUs!
If you want to enable quantized LoRA (QLoRA) on the Windows platform, you should install a pre-built version of bitsandbytes library, which supports CUDA 11.1 to 12.1.
pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.39.1-py3-none-win_amd64.whlPlease 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_bash.py \
--stage pt \
--model_name_or_path path_to_your_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_bash.py \
--stage sft \
--model_name_or_path path_to_your_model \
--do_train \
--dataset alpaca_gpt4_en \
--finetuning_type lora \
--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 \
--plot_loss \
--fp16CUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage rm \
--model_name_or_path path_to_your_model \
--do_train \
--dataset comparison_gpt4_en \
--finetuning_type lora \
--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_bash.py \
--stage ppo \
--model_name_or_path path_to_your_model \
--do_train \
--dataset alpaca_gpt4_en \
--finetuning_type lora \
--checkpoint_dir 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_bash.py # arguments (same as above)Example configuration for full-tuning with DeepSpeed ZeRO-2
compute_environment: LOCAL_MACHINE
deepspeed_config:
gradient_accumulation_steps: 4
gradient_clipping: 0.5
offload_optimizer_device: none
offload_param_device: none
zero3_init_flag: false
zero_stage: 2
distributed_type: DEEPSPEED
downcast_bf16: 'no'
machine_rank: 0
main_training_function: main
mixed_precision: fp16
num_machines: 1
num_processes: 4
rdzv_backend: static
same_network: true
tpu_env: []
tpu_use_cluster: false
tpu_use_sudo: false
use_cpu: falseCUDA_VISIBLE_DEVICES=0 python src/train_bash.py \
--stage pt \
--model_name_or_path path_to_your_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 at 4/8-bit evaluation.
python src/xxx_demo.py \
--model_name_or_path path_to_your_model \
--checkpoint_dir path_to_checkpointpython src/export_model.py \
--model_name_or_path path_to_your_model \
--checkpoint_dir path_to_checkpoint \
--output_dir path_to_exportThis repository is licensed under the Apache-2.0 License.
Please follow the model licenses to use the corresponding model weights:
If this work is helpful, please kindly 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.