llmtuner 0.1.1

Easy-to-use fine-tuning framework using PEFT

LLaMA Efficient Tuning

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Changelog

[23/07/18] Now we develop an all-in-one Web UI for training, evaluation and inference. Try train_web.py to fine-tune models in your Web browser. Thank @KanadeSiina and @codemayq for their efforts in the development.

[23/07/11] Now we support training the Baichuan-13B model in this repo. Please replace the Baichuan-13B model file with tests/modeling_baichuan.py and try --model_name_or_path path_to_baichuan_model 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.

Supported Models

• LLaMA (7B/13B/33B/65B)
• BLOOM & BLOOMZ (560M/1.1B/1.7B/3B/7.1B/176B)
• Falcon (7B/40B)
• Baichuan (7B/13B)
• InternLM (7B)

Supported Training Approaches

• (Continually) pre-training
  • Full-parameter tuning
  • Partial-parameter tuning
  • LoRA
  • QLoRA
• Supervised fine-tuning
  • Full-parameter tuning
  • Partial-parameter tuning
  • LoRA
  • QLoRA
• RLHF
  • LoRA
  • QLoRA

Provided Datasets

• For pre-training:
  • Wiki Demo
• 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:
  • HH-RLHF
  • Open Assistant
  • Open Assistant (Chinese)
  • GPT-4 Generated Data
  • GPT-4 Generated Data (Chinese)

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

Requirement

• 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.whl

Getting Started

Data Preparation (optional)

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.

Dependence Installation (optional)

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

All-in-one Web UI

python src/train_web.py

(Continually) Pre-Training

CUDA_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 \
    --fp16

Supervised Fine-Tuning

CUDA_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 \
    --fp16

Reward Model Training

CUDA_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 \
    --fp16

PPO Training (RLHF)

CUDA_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_loss

Distributed Training

accelerate 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: false

Evaluation (BLEU and ROUGE_CHINESE)

CUDA_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_generate

We recommend using --per_device_eval_batch_size=1 and --max_target_length 128 at 4/8-bit evaluation.

API Demo

python src/api_demo.py \
    --model_name_or_path path_to_your_model \
    --checkpoint_dir path_to_checkpoint

See http://localhost:8000/docs for API documentation.

CLI Demo

python src/cli_demo.py \
    --model_name_or_path path_to_your_model \
    --checkpoint_dir path_to_checkpoint

Web Demo

python src/web_demo.py \
    --model_name_or_path path_to_your_model \
    --checkpoint_dir path_to_checkpoint

Export model

python src/export_model.py \
    --model_name_or_path path_to_your_model \
    --checkpoint_dir path_to_checkpoint \
    --output_dir path_to_export

License

This repository is licensed under the Apache-2.0 License.

Please follow the model licenses to use the corresponding model weights:

• LLaMA
• BLOOM
• Falcon
• baichuan
• InternLM

Citation

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}
}

Acknowledgement

This repo is a sibling of ChatGLM-Efficient-Tuning. They share a similar code structure of efficient tuning on large language models.

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