## 🦙🌲🤏 Alpaca-LoRA: Low-Rank LLaMA Instruct-Tuning

This repository contains code for reproducing the [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) results using [low-rank adaptation (LoRA)](https://arxiv.org/pdf/2106.09685.pdf).
The fine-tuning runs within five hours on a consumer GPU,
and the LoRA weights are made available on the Huggingface model hub.
With Huggingface's out-of-the-box 8-bit quantization,
we aim to provide an Instruct model of similar quality to `text-davinci-003` that can run [on a Raspberry Pi](https://twitter.com/miolini/status/1634982361757790209). (For research.)

Until Jason Phang's [LLaMA implementation](https://github.com/huggingface/transformers/pull/21955)
is merged, users will need to replace their local Huggingface `transformers` as described below.
For fine-tuning LoRAs we use Huggingface's [PEFT](https://github.com/huggingface/peft).
Included also is code to download the LLaMA foundation model from the Huggingface model hub. (For research.)
Once I've finished running the finetuning code myself, I'll put the LoRA on the Hub as well, and the code in `generate.py` should work as expected.

### Setup

1. Install dependencies (**install zphang's transformers fork**)

```
pip install -q datasets loralib sentencepiece

pip uninstall transformers
pip install -q git+https://github.com/zphang/transformers@c3dc391

pip install -q git+https://github.com/huggingface/peft.git
```

2. [Install bitsandbytes from source.](https://github.com/TimDettmers/bitsandbytes/blob/main/compile_from_source.md)

### Inference (`generate.py`)

See `generate.py`. This file reads the `decapoda-research/llama-7b-hf` model from the Huggingface model hub and the LoRA weights from `tloen/alpaca-lora-7b`, and runs inference on a specified input. Users should treat this as example code for the use of the model, and modify it as needed.

### Training (`finetune.py`)

Under construction. If you're impatient, note that this file contains a set of hardcoded hyperparameters you should feel free to modify.
PRs adapting this code to multi-GPU setups and larger models are always welcome.

### To do

- [ ] Merge LoRA weights into LLaMA weights to remove inference dependency on PEFT
- [ ] Train/val/test split
- [ ] Hyperparameter tuning code
- [ ] Documentation for notebook
- [ ] Support for `13b`, `30b`, `65b`
- [ ] Train a version that doesn't waste tokens on the prompt header
- [ ] Inference CLI and evaluation
- [ ] Better disclaimers about why using LLaMA without permission is very bad!