File size: 3,127 Bytes
256aed0
806d367
256aed0
 
 
 
806d367
256aed0
 
 
806d367
e7ee027
2337c3d
97d5aae
b7ef03e
 
2337c3d
806d367
8dcad5e
 
e7ee027
 
 
 
 
 
 
 
 
 
 
f95cdd8
e7ee027
 
 
 
 
dd9ebcd
bc97c87
e7ee027
 
 
e0515e1
e7ee027
 
806d367
 
e7ee027
806d367
e7ee027
 
e0515e1
 
 
 
 
 
66802fa
e7ee027
 
806d367
 
e7ee027
 
 
66802fa
e7ee027
 
 
 
 
806d367
e7ee027
806d367
e7ee027
 
 
806d367
66802fa
806d367
66802fa
e7ee027
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
---
title: Simple LLM Finetuner
emoji: 🦙
colorFrom: yellow
colorTo: orange
sdk: gradio
app_file: app.py
pinned: false
---

# 🦙 Simple LLM Finetuner

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lxe/simple-llama-finetuner/blob/master/Simple_LLaMA_FineTuner.ipynb)
[![Open In Spaces](https://img.shields.io/badge/🤗-Open%20In%20Spaces-blue.svg)](https://huggingface.co/spaces/lxe/simple-llama-finetuner)
[![](https://img.shields.io/badge/no-bugs-brightgreen.svg)](https://github.com/lxe/no-bugs) 
[![](https://img.shields.io/badge/coverage-%F0%9F%92%AF-green.svg)](https://github.com/lxe/onehundred/tree/master)

Simple LLM Finetuner is a beginner-friendly interface designed to facilitate fine-tuning various language models using [LoRA](https://arxiv.org/abs/2106.09685) method via the [PEFT library](https://github.com/huggingface/peft) 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.

## Acknowledgements

 - https://github.com/zphang/minimal-llama/
 - https://github.com/tloen/alpaca-lora
 - https://github.com/huggingface/peft

## Features

- 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

## Getting Started

### Prerequisites

- Linux or WSL
- Modern NVIDIA GPU with >= 16 GB of VRAM (but it might be possible to run with less for smaller sample lengths)

### Usage

I recommend using a virtual environment to install the required packages. Conda preferred.

```
conda create -n simple-llm-finetuner python=3.10
conda activate simple-llm-finetuner
conda install -y cuda -c nvidia/label/cuda-11.7.0
conda install -y pytorch=2 pytorch-cuda=11.7 -c pytorch
```

On WSL, you might need to install CUDA manually by following [these steps](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local), 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-llm-finetuner.git
cd simple-llm-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 new LoRA adapter name in the "New PEFT Adapter 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/` directory.

After training is done, navigate to "Inference" tab, select your LoRA, and play with it.

Have fun!

## YouTube Walkthough

https://www.youtube.com/watch?v=yM1wanDkNz8

## License

MIT License