---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---

# LoftQ Initialization

| [Paper](https://arxiv.org/abs/2310.08659) | [Code](https://github.com/yxli2123/LoftQ) | [PEFT Example](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning) |

LoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W.

This model, `LoftQ/Llama-2-7b-hf-fp16-64rank-gsm8k`, is LoRA fine-tuned from [LLAMA-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) on [GSM8K](https://huggingface.co/datasets/gsm8k) dataset.

## Model Info
### LoRA adapters
- rank: 64
- lora_alpha: 16
- target_modules: ["down_proj", "up_proj", "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj"]

## Usage

**Inference** Here is an example code for inference after the model has been fine-tuned on [GSM8K](https://huggingface.co/datasets/gsm8k).

```python
import torch
from transformers import AutoModelForCausalLM

MODEL_ID = "LoftQ/Llama-2-7b-hf-fp16-64rank-gsm8k"

model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, 
    torch_dtype=torch.bfloat16,  # you may change it with different models
    token=YOUR_HF_TOKEN,
)
# you can also merge the LoRA adapters to the backbone if you like
model = model.merge_and_unload()

# Do inference with `model` ...
```

See full evaluation on GSM8K on [Github](https://github.com/yxli2123/LoftQ/blob/main/test_gsm8k.py).

## Experiment Results
We have conducted experiments on supervised fine-tuning of [GSM8K](https://huggingface.co/datasets/gsm8k) 
and [WikiText-2](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1).

| Model          | Bits | Rank | LoRA Initial         | GSM8K | 
| -------------- | ---- | ---- | -------------------- | ----- |
| **LLAMA-2-7b** | 16   | 64   | Gaussian + 0         | 36.9  |
| LLAMA-2-7b     | 4    | 64   | Gaussian + 0 (QLoRA) | 35.1  | 
| LLAMA-2-7b     | 4    | 64   | LoftQ                | 35.0  | 


## Citation

```bibtex
@article{li2023loftq,
  title={Loftq: Lora-fine-tuning-aware quantization for large language models},
  author={Li, Yixiao and Yu, Yifan and Liang, Chen and He, Pengcheng and Karampatziakis, Nikos and Chen, Weizhu and Zhao, Tuo},
  journal={arXiv preprint arXiv:2310.08659},
  year={2023}
}
```