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LMFlow

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.

Latest News

• [2024-12-02] Support Hymba, a new family of small language models featuring a hybrid-head parallel architecture. Check out Post-training Hymba for more details.
• [2024-07-01] 🏆 LMFlow receives the Best Demo Paper Award at NAACL 2024! 🎉
• [2024-06-30] Expanding Optimization Options! We now support custom optimizer training with a variety of optimizers. Dive into the details and try out the new features with our updated script at custom_optimizers.
• [2024-04-25] :rocket: Support conversation template! We've preset the latest Llama-3 and Phi-3 conversation templates as well as some frequently used templates such as chatml (see all templates here), and we are working on adding more preset templates. Adding corresponding --conversation_template in the shell script and you are all set! :rocket:
• [2024-03-27] Support LISA, enabling 7B training in 24G memory without offloading!
• [2023-09-11] Support speculative decoding. Check out speculative_decoding for the usage and acceleration details.
• [2023-08-14] Support long context inference with position interpolation (Linear & NTK scaling ) for LLaMA models. Check out postion_interpolation for more details.
• [2023-08-07] Support Flash Attention-2. Check out flash_attention for more details.

• [2023-08-02] Support Llama2, ChatGLM2, and Baichuan models.

• [2023-07-23] LMFlow multimodal chatbot is now available! Support multimodal inputs of images and texts. Online Demo is also provided (We hold the service on a single GPU, hence one may experience "queuing" or "application busy" sometimes when multiple users are accessing at the same time, please wait and attempt again later when such event happens)

• [2023-06-22] LMFlow paper is out! Check out our implementation details at https://arxiv.org/abs/2306.12420

• [2023-06-16] Our finetuned Robin-33B-V2 scored an impressive 64.1 on the Huggingface LLM leaderboard in our offline evaluation, outperforming major open-source LLMs! All checkpoints (7B, 13B, 33B, and 65B) are released! Checkout the performance here.

• [2023-06-07] LMFlow is now officially available on PyPI! Install it with pip install lmflow-finetune!

• [2023-05-30] Release Robin-13B-v2 and Robin-33B-v2!

• [2023-05-15] Release LMFlow-data, the training dataset of Robin-7B-v2. A new test data is also released.

• [2023-05-09] Release Robin-7B-v2, achieving competitive performance on chitchat, commonsense reasoning and instruction-following tasks. Refer to our comprehensive study.

• [2023-05-08] Release LMFlow Benchmark, an automatic evaluation framework for open-source chat-style LLMs. Benchmark results on 31 popular models are reported. Participate in LMFlow Benchmark.

• [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. [Paper]

• [2023-04-02] Web service is online!

• [2023-04-01] Release three instruction-tuned checkpoints and three medical checkpoints in model zoo: LLaMA-7B-tuned, LLaMA-13B-tuned, LLaMA-33B-tuned, 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! Our tasked-tuned model beats ChatGPT on medical domain.

Table of Contents

• LMFlow
  • Latest News
  • Table of Contents
  • Supported Models
  • Quick Start
    • Setup
    • Prepare Dataset
    • Finetuning
      • Full Finetuning
      • LISA
      • LoRA
    • Inference
    • Deployment
    • Evaluation
  • Supported Features
  • Support
  • License
  • Citation

Supported Models

Quick Start

Setup

Our package has been tested on Linux OS (Ubuntu 20.04). Other OS platforms (MacOS, Windows) are not fully tested, where you may encounter unexpected errors. If you are using LMFlow for the first time, we recommend you to try on a Linux machine or Google Colab.

CUDA versions 10.3-11.7 are supported in versions v0.0.5 or older. For CUDA versions greater than 11.7, one can use our stable branch >= v0.0.6.

git clone -b v0.0.9 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 .

[!TIP] We use WandB to track and visualize the training process by default. Before running the training scripts, users may need to log in to WandB using the command:

wandb login

For detailed instructions, refer to the WandB Quickstart Guide. Step 1 (registration) and Step 2 (login using your WandB API key) should be sufficient to set up your environment.

<details><summary>Disabling wandb</summary>

One can disable wandb by either:

1. Adding environment variable before running the training command.

export WANDB_MODE=disabled

2. OR, specifying the integrations to report the results and logs to. In the training script, add:

--report_to none \

</details>

Prepare Dataset

Please refer to our doc.

Finetuning

Full Finetuning

Full training updates all the parameters to finetune a language model. Here is an example to finetune a GPT-2 base model.

cd data && ./download.sh alpaca && cd -

bash ./scripts/run_finetune.sh \
  --model_name_or_path gpt2 \
  --dataset_path data/alpaca/train_conversation \
  --output_model_path output_models/finetuned_gpt2

[!TIP] For conversation dataset, specify a conversation template for better performance by adding --conversation_template to the command.

<details><summary>Llama-3-8B conversation dataset example</summary>

cd data && ./download.sh alpaca && cd -

bash ./scripts/run_finetune.sh \
 --model_name_or_path meta-llama/Meta-Llama-3-8B \
 --dataset_path data/alpaca/train_conversation \
 --conversation_template llama3 \
 --output_model_path output_models/finetuned_llama3_8b

</details>

LISA

LISA is a memory-efficient finetuning algorithm that allows tradeoff between memory and the number of randomly unfreezed layers. This script currently is only tested in single gpus. Please stay tuned for our latest updates :smile:

cd data && ./download.sh alpaca && cd -

bash ./scripts/run_finetune_with_lisa.sh \
  --model_name_or_path meta-llama/Llama-2-7b-hf \
  --dataset_path data/alpaca/train_conversation \
  --output_model_path output_models/finetuned_llama2_7b \
  --lisa_activated_layers 1 \
  --lisa_interval_steps 20

[!TIP]

<details><summary>Llama-2-7B conversation dataset example</summary>

cd data && ./download.sh alpaca && cd -

bash ./scripts/run_finetune_with_lisa.sh \
 --model_name_or_path meta-llama/Llama-2-7b-hf \
 --dataset_path data/alpaca/train_conversation \
 --conversation_template llama2 \
 --output_model_path output_models/finetuned_llama2_7b_lisa \
 --lisa_activated_layers 1 \
 --lisa_interval_steps 20

</details>

LoRA

LoRA is a parameter-efficient finetuning algorithm and is more efficient than full finetuning.

cd data && ./download.sh alpaca && cd -

bash ./scripts/run_finetune_with_lora.sh \
  --model_name_or_path facebook/galactica-1.3b \
  --dataset_path data/alpaca/train_conversation \
  --output_lora_path output_models/finetuned_galactica_lora

[!TIP]

<details><summary>Llama-2-7B conversation dataset example</summary>

cd data && ./download.sh alpaca && cd -

bash ./scripts/run_finetune_with_lora.sh \
 --model_name_or_path meta-llama/Llama-2-7b-hf \
 --dataset_path data/alpaca/train_conversation \
 --conversation_template llama2 \
 --output_model_path output_models/finetuned_llama2_7b_lora \

</details> <details><summary>Merge LoRA Weight</summary>

Merge LoRA weight and the base model into one using:

bash ./scripts/run_merge_lora.sh \
 --model_name_or_path Qwen/Qwen1.5-1.8B \
 --lora_model_path output_models/lora \
 --output_model_path output_models/lora_merged \

</details>

Inference

After finetuning, you can run the following command to chat with the model.

bash ./scripts/run_chatbot.sh output_models/finetuned_gpt2

[!TIP] We recommend using vLLM for faster inference.

<details><summary>Faster inference using vLLM</summary>

bash ./scripts/run_vllm_inference.sh \
  --model_name_or_path Qwen/Qwen2-0.5B \
  --dataset_path data/alpaca/test_conversation \
  --output_dir data/inference_results \

</details>

Deployment

If you want to deploy your own model locally, we 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

Evaluation

LMFlow Benchmark is an automatic evaluation framework for open-source large language models. We use negative log likelihood (NLL) as the metric to evaluate different aspects of a language model: chitchat, commonsense reasoning, and instruction following abilities.

You can directly run the LMFlow benchmark evaluation to obtain the results to participate in the LLM comparision. For example, to run GPT2 XL, one may execute

bash ./scripts/run_benchmark.sh --model_name_or_path gpt2-xl

--model_name_or_path is required, you may fill in huggingface model name or local model path here.

To check the evaluation results, you may check benchmark.log in ./output_dir/gpt2-xl_lmflow_chat_nll_eval, ./output_dir/gpt2-xl_all_nll_eval and ./output_dir/gpt2-xl_commonsense_qa_eval.

Supported Features

• LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning

  LISA is a novel and memory-efficient training strategy for large language models that outperforms existing methods like LoRA by selectively freezing layers during optimization. Check out LISA for more details.
  In LMFLow, activate LISA using --use_lisa 1 in your training command. Control the number of activation layers with --lisa_activated_layers 2, and adjust the freezing layers interval using --lisa_step_interval 20.

• LoRA

  LoRA is a parameter-efficient finetuning algorithm and is more efficient than full finetuning. Check out finetuning-lora for more details.

• FlashAttention

  LMFlow supports both FlashAttention-1 and the latest FlashAttention-2. Check out flash_attention for more details.

• Gradient Checkpointing

  Gradient checkpointing is a memory optimization technique that trades compute for memory. It is useful when the model is too large to fit into GPU memory. Use it by just adding --gradient_checkpointing to your training command.

• Deepspeed Zero3

  LMFlow supports Deepspeed Zero-3 Offload. We provide an example deepspeed config, and you can directly use it.

• 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.

• FlashAttention

  LMFlow supports both FlashAttention-1 and the latest FlashAttention-2. Check out flash_attention for more details.

• vLLM

  Try vLLM for fast and easy-to-use LLM inference and serving. Thanks for the great work!

• Position Interpolation for LLaMA Models

  Now LMFlow supports the latest Linear & NTK (Neural Kernel theory) scaling techniques for LLaMA models. Check out postion_interpolation for more details.

• Vocabulary Extension

  Now you can train your own sentencepiece tokenizer and merge it with model's origin hf tokenizer. Check out vocab_extension for more details.

• Multimodal Chatbot

  LMFlow supports multimodal inputs of images and texts. Check out our LMFlow multimodal chatbot.

• Custom Optimization

  LMFlow now supports custom optimizer training with a variety of optimizers. Elevate your model's performance with tailored optimization strategies. Dive into the details and try out the new features with our updated script at custom_optimizers.

  The following table evaluates the performance of custom optimizers in the fine-tuning process of GPT-2 on the Alpaca dataset, emphasizing their individual impacts on the training loss. The specific hyperparameter settings utilize default configurations, which can be customized and adjusted at custom_optimizers. It is important to note that the evaluations were conducted over a duration of 0.1 epochs to provide a preliminary insight into the optimizers' effectiveness.

  Optimizer Name	Train Loss
  RMSprop	2.4016
  LION-32bit	2.4041
  Adam	2.4292
  AdamP	2.4295
  AdamW	2.4469
  AdaFactor	2.4543
  AdaBound	2.4547
  AdamWScheduleFree	2.4677
  Adan	2.5063
  NAdam	2.5569
  AdaBelief	2.5857
  AdaMax	2.5924
  RAdam	2.6104
  AdaDelta	2.6298
  AdaGrad	2.8657
  Yogi	2.9314
  NovoGrad	3.1071
  Sophia	3.1517
  LAMB	3.2350
  LARS	3.3329
  SGDScheduleFree	3.3541
  SGDP	3.3567
  SGD	3.3734

Support

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

License

The code included in this project is licensed under the Apache 2.0 license. If you wish to use the codes and models included in this project for commercial purposes, please sign this document to obtain authorization.

Citation

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

@article{diao2023lmflow,
  title={Lmflow: An extensible toolkit for finetuning and inference of large foundation models},
  author={Diao, Shizhe and Pan, Rui and Dong, Hanze and Shum, Ka Shun and Zhang, Jipeng and Xiong, Wei and Zhang, Tong},
  journal={arXiv preprint arXiv:2306.12420},
  year={2023}
}

@article{dong2023raft,
  title={Raft: Reward ranked finetuning for generative foundation model alignment},
  author={Dong, Hanze and Xiong, Wei and Goyal, Deepanshu and Pan, Rui and Diao, Shizhe and Zhang, Jipeng and Shum, Kashun and Zhang, Tong},
  journal={arXiv preprint arXiv:2304.06767},
  year={2023}
}

@article{pan2024lisa,
  title={LISA: Layerwise Importance Sampling for Memory-Efficient Large Language Model Fine-Tuning}, 
  author={Pan, Rui and Liu, Xiang and Diao, Shizhe and Pi, Renjie and Zhang, Jipeng and Han, Chi and Zhang, Tong},
  journal={arXiv preprint arXiv:2403.17919},
  year={2024}
}