LMFlow

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An extensible, convenient, and efficient toolbox for finetuning large machine learning models, designed to be user-friendly, speedy and reliable, and accessible to the entire community.

Large Model for All. See our vision.

Latest News

[2023-04-21] 🚀Release Robin-7B (based on LLaMA-7B), and two models for commercial use: Parakeets-2.7B (based on GPT-NEO-2.7B) and Cokatoo-7B (based on StableLM-7B) Download here 🚀
[2023-04-15] Inference: Support streaming output and ChatGLM.
[2023-04-10] We propose a new alignment algorithm: Reward rAnked FineTuning (RAFT), which is more efficient than conventional (PPO-based) RLHF. The movie review demo are available now. [Paper]
[2023-04-02] Web service is online!
[2023-04-01] Release Chinese checkpoints in model zoo: LLaMA-7B-tuned, LLaMA-13B-tuned, LLaMA-33B-tuned.
[2023-04-01] Release English checkpoints in model zoo: LLaMA-7B-medical, LLaMA-13B-medical, and LLaMA-33B-medical.
[2023-03-27] Support full tuning and lora tuning for all decoder models.
[2023-03-27] Tasked tuned model beats ChatGPT on medical domain.
[2023-03-27] Release code and checkpoints - version 0.0.1

Demos

We provide four kinds of demos which include

Online Service: If you don't want to run any code and just want to try our models, we deploy our instruction-tuned LLaMA you to have a try.
Colab Chatbot (shell): An interactive shell-based chatbot for you to easily deploy a chatbot on colab.
Colab Chatbot (web): An interactive web-based chatbot for you to easily deploy your own chatbot on colab.
Local Deploy: We also provide a way for you to deploy your model/chatbot locally, which means you can deploy much larger model than previous three methods if you have enough resource.

Online Service

Welcome to visit our web service. We deploy LLaMA-7B-tuned model online for preview. Due to the high website traffic, sometimes the website may fail to respond. You can also deploy the chatbot following Local Deploy.

Colab chatbot (shell)

We provide a simple shell demo of chatbot with Google Colab's T4/P100/V100 GPU. Notice that the provided gpt-neo-2.7b model is a rather weak model, which only supports English and may sometimes generate unsatisfactory responses. To improve the performance, users can use their own dataset to finetune and obtain a better model with LMFlow. One can also try other available decoder-only models provided in 🤗 Hugging Face, by

./scripts/run_chatbot.sh {another-model-name}

Colab chatbot(web)

We provide a simple web demo of chatbot with Google Colab's T4/P100/V100 GPU. Notice that the provided gpt-neo-2.7b model is a rather weak model, which only supports English and may sometimes generate unsatisfactory responses.

Local Deploy

If you have resources and want to deploy your own model locally. We provide you an easy way to run a flask server to launch a backend (to further provide services to other frontend) and an interactive web frontend (to let you communicate directly) by

cd ./service
python app.py

We also provide a gradio-based UI for building chatbots. Running the following command will launch the demo for robin-7b:

pip install gradio
python ./examples/chatbot_gradio.py --deepspeed configs/ds_config_chatbot.json --model_name_or_path YOUR-LLAMA  --lora_model_path ./robin-7b --prompt_structure "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: {input_text}###Assistant:"       --end_string "#" --max_new_tokens 200

We also hosted it on Hugging Face Space.

Colab RAFT-diffusion

We also provide a simple demo to display the effectiveness of RAFT algorithm on diffusion models. You can refer to either Colab link or experimental/RAFT-diffusion/SD256-RAFT.ipynb. The support of multi-modal training of LMFlow is under development.

Medical Performance

	PubMedQA (ID)	MedQA-USMLE (OOD)	MedMCQA (ID)	Average
Human (pass)	60.0	50.0
Human (expert)	78.0	87.0	90.0	85.0

InstructGPT 175B	73.2	46.0	44.0	54.4
ChatGPT	63.9	57.0	44.7	55.2
LLaMA 7B	5.2	27.1	24.3	18.9
LLaMA 33B	1.8	43.4	30.3	25.2

Task-tuned LLaMA 7B (Full)	75.1	44.5	49.9	56.5
Task-tuned LLaMA 33B (LoRA)	74.0	51.3	50.2	58.5

The LLaMA 33B (LoRA) performance is achieved with only ~16h finetuning on the training split of PubMedQA and MedMCQA with a single 8 * A100 server. For more performance, including instruction tuning results, please refer to our Documentation.

Model Zoo

We open-sourced the trained checkpoints to everyone for further training and inference.


Model Base Model	Robin-7B ⭐ LLaMA-7B	Robin-13B LLaMA-13B	Robin-33B LLaMA-33B	Robin-65B LLaMA-65B

Model Base Model	Robin-7B-medical LLaMA-7B	Robin-13B-medical LLaMA-13B	Robin-33B-medical LLaMA-33B	Robin-65B-medical LLaMA-65B

Model Base Model	Parakeets-2.7B ⭐ GPT-NEO-2.7B	Cockatoo-3B StableLM-3B	Cockatoo-7B ⭐ StableLM-7B

Supported Pipelines

Pipelines	Status
Task Tuning	✅ Supported
Instruction Tuning	✅ Supported
Parameter-Efficient Tuning	✅ Supported
Large Model Inference	✅ Supported
Alignment Tuning	✅ Supported

Supported Models

Seamlessly supported all the decoder models in 🤗 Hugging Face. LLaMA, GPT2, GPT-Neo, Galactica, have been fully tested. We will support encoder models soon.

1.Setup

Our package has been full tested on Linux OS (Ubuntu 20.04). Other OS platforms (MacOS, Windows) are not fully tested. You may encounter some unexpected errors. You may try it first on a Linux machine or use Google Colab to experience it.

git clone https://github.com/OptimalScale/LMFlow.git
cd LMFlow
conda create -n lmflow python=3.9 -y
conda activate lmflow
conda install mpi4py
pip install -e .

2.Prepare Dataset

Please refer to our doc.

3. Run Scripts

3.1 Run Finetuning

You can run scripts/run_finetune.sh to finetune a GPT-2 base model

./scripts/run_finetune.sh

If you would like to provide arguments for deepspeed to reflect your machine settings, you may pass the corresponding deepspeed arguments to the script. For example,

./scripts/run_finetune.sh "--num_gpus=8 --master_port 10001"

To enable LoRA finetuning, you may refer to

./scripts/run_finetune_with_lora.sh

which can be run in similar manner.

For detailed configurations, one may modify these scripts directly. These scripts actually just call python script examples/finetune.py, which can be run in following manner,

deepspeed ${deepspeed_args} \
  examples/finetune.py \
    --deepspeed configs/ds_config_zero3.json \
    --bf16 \
    --run_name finetune_with_lora \
    --model_name_or_path facebook/galactica-1.3b \
    --num_train_epochs 0.01 \
    --learning_rate 2e-5 \
    --dataset_path ${dataset_path} \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 1 \
    --validation_split_percentage 0 \
    --logging_steps 20 \
    --block_size 512 \
    --do_train \
    --output_dir output_models/finetune \
    --overwrite_output_dir \
    --ddp_timeout 72000 \
    --save_steps 5000 \
    --dataloader_num_workers 1

Here we set number of epochs --num_train_epochs to 0.01 so that the finetuning process can be finished quickly. If you wish to obtain a model with better performance, feel free to adjust those hyperparameters. You may run

python examples/finetune.py -h

to view all possible finetuning arguments. The finetuned model checkpoint will be saved in the argument specified by --output_dir, which is output_models/finetune in the above example. We follow Alpaca and Vicuna in the model tuning process and serve the model in our web service.

3.2 Run Evaluation

One can directly run evaluation with an existing Hugging Face model, e.g. to run GPT2 large, one may execute

./scripts/run_evaluation.sh

or run the corresponding python script

CUDA_VISIBLE_DEVICES=0 \
    deepspeed examples/evaluate.py \
    --answer_type medmcqa \
    --model_name_or_path gpt2-large \
    --dataset_path data/MedQA-USMLE/validation \
    --deepspeed examples/ds_config.json

To load the finetuned model, specify --model_name_or_path with the saved model checkpoint directory path.

For LoRA finetuned models, one may refer to

./scripts/run_evaluation_with_lora.sh

Those scripts invoke the examples examples/*.py built based on our APIs. For more API-related examples, one may refer to the methods in the unittest tests.

4. Additional Notes

4.1 LLaMA Checkpoint

Please refer to our doc.

4.2 DeepSpeed Config

You can config the deepspeed under configs. Details can be referred at DeepSpeed Configuration

4.3 LLaMA Inference on CPU

Thanks to the great efforts of llama.cpp. It is possible for everyone to run their LLaMA models on CPU by 4-bit quantization. We provide a script to convert LLaMA LoRA weights to .pt files. You only need to use convert-pth-to-ggml.py in llama.cpp to perform quantization.

5. Model Release

5.1 Medical Model Checkpoints

You can run following script to download our medical model checkpoints :

cd output_models
bash download.sh medical_ckpt
cd -

You can also directly download our model via google drive link : medical_ckpt.tar.gz

5.2 Instruction Model Checkpoints

Similarly, you can run following script to download our instruction model checkpoints :

cd output_models
bash download.sh instruction_ckpt
cd -

You can also directly download our model via google drive link : instruction_ckpt.tar.gz

5.3 Reproduce the result

After downloading the model checkpoints, you can replace the --lora_model_path with output_models/instruction_ckpt/llama7b-lora (example for llama-7b for instruction) and replace --model_name_or_path with your converted llama model inside LMFlow/scripts/run_evaluation_with_lora.sh and run this shell script to reproduce the result.

Then you can check the model performance at our Doc.

Documentation

Please refer to our Documentation for more API reference and experimental results.

Vision

Hello there! We are excited to announce the upcoming release of our code repository that includes a complete LLM training process, enabling users to quickly build their own language models and train them effectively.

Our code repository is not just a simple model; it includes the complete training workflow, model optimization, and testing tools. You can use it to build various types of language models, including conversation models, question-answering models, and text generation models, among others.

Moreover, we aim to create an open and democratic LLM sharing platform where people can share their checkpoints and experiences to collectively improve the skills of the community. We welcome anyone who is interested in LLM to participate and join us in building an open and friendly community!

Whether you are a beginner or an expert, we believe that you can benefit from this platform. Let's work together to build a vibrant and innovative LLM community!

Acknowledgement

LMFlow draws inspiration from various studies, including but not limited to:

Alpaca: https://github.com/tatsu-lab/stanford_alpaca
Vicuna: https://github.com/lm-sys/FastChat

Support

If you need any help, please submit a Github issue.

Contributors

Citation

If you find this repository useful, please consider giving ⭐ and citing:

@misc{lmflow,
  author = {Shizhe Diao and Rui Pan and Hanze Dong and KaShun Shum and Jipeng Zhang and Wei Xiong and Tong Zhang},
  title = {LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://optimalscale.github.io/LMFlow/}},
}

after installation of bitsandbytes, go to ~/anaconda3/envs/lmflow/lib/python3.9/site-packages/bitsandbytes and run cp libbitsandbytes_cuda117.so libbitsandbytes_cpu.so

Seratna/LMFlow