Simple LLaMA Finetuner is a beginner-friendly interface designed to facilitate fine-tuning the LLaMA-7B language model using LoRA method via the PEFT library on commodity NVIDIA GPUs. With small dataset and sample lengths of 256, you can even run this on a regular Colab Tesla T4 instance.
With this intuitive UI, you can easily manage your dataset, customize parameters, train, and evaluate the model's inference capabilities.
- https://github.com/zphang/minimal-llama/
- https://github.com/tloen/alpaca-lora
- https://github.com/huggingface/peft
- https://huggingface.co/datasets/Anthropic/hh-rlhf
- Simply paste datasets in the UI, separated by double blank lines
- Adjustable parameters for fine-tuning and inference
- Beginner-friendly UI with explanations for each parameter
- Accelerate / DeepSpeed
- Load other models
- More dataset preparation tools
- Linux or WSL
- Modern NVIDIA GPU with >16 GB of VRAM (but it might be possible to run with less for smaller sample lengths)
I recommend using a virtual environment to install the required packages. Conda preferred.
conda create -n llama-finetuner python=3.10
conda activate llama-finetuner
conda install -y cuda -c nvidia/label/cuda-11.7.0
conda install -y pytorch=1.13.1 pytorch-cuda=11.7 -c pytorch
On WSL, you might need to install CUDA manually by following these steps, then running the following before you launch:
export LD_LIBRARY_PATH=/usr/lib/wsl/lib
Clone the repository and install the required packages.
git clone https://github.com/lxe/simple-llama-finetuner.git
cd simple-llama-finetuner
pip install -r requirements.txt
Launch it
python main.py
Open http://127.0.0.1:7860/ in your browser. Prepare your training data by separating each sample with 2 blank lines. Paste the whole training dataset into the textbox. Specify the model name in the "LoRA Model Name" textbox, then click train. You might need to adjust the max sequence length and batch size to fit your GPU memory. The model will be saved in the lora-{your model name} directory.
After training is done, navigate to "Inference" tab, click "Reload Models", select your model, and play with it.
Have fun!
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- This is a cli version of simple-llama-finetuner.
- This cli version is provided by @chaignc from Hacker AI Team.
- Thank you to the original author @lxe, who did the real work :D
$ ./main_cli.py -h
Usage: main_cli.py command [args...]
Commands:
predict
train
$ ./main_cli.py train -h
Usage: main-cli.py train [OPTIONS]
Options:
--epochs=INT (default: 1)
--gradient-accumulation-steps=INT (default: 1)
--learning-rate=FLOAT (default: 0.0003)
--lora-alpha=INT (default: 16)
--lora-dropout=FLOAT (default: 0.01)
--lora-r=INT (default: 8)
--max-seq-length=INT (default: 512)
--micro-batch-size=INT (default: 1)
--model-name=STR (default: elderberry-cherry)
--training-file=STR (default: ./example-datasets/leo.txt)
Other actions:
-h, --help Show the help
$ ./main_cli.py predict -h
Usage: ./main_cli.py predict [OPTIONS]
Options:
--inference-text=STR (default: What is leo?)
--max-new-tokens=INT (default: 50)
--model-name=STR (default: ./lora-elderberry-cherry)
--repeat-penalty=INT (default: 1)
--temperature=FLOAT (default: 0.01)
--top-k=INT (default: 50)
--top-p=FLOAT (default: 0.3)
Other actions:
-h, --help Show the help
$ ./main_cli.py train --training-file ./example-datasets/leo.txt
$ ./main_cli.py predict --inference_text "C'est qui Paulivan"
Un gros faignant
MIT License
Copyright (c) 2023 Aleksey Smolenchuk
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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