LoftQ's picture
Update README.md
7203303 verified
---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- 'quantization '
- lora
- loftq
- llama
---
# 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, `Meta-Llama-3-70B-4bit-64rank`, is obtained from [LLAMA-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B).
The backbone is under `LoftQ/Meta-Llama-3-70B-4bit-64rank` and LoRA adapters are under the `subfolder='loftq_init'`.
## Model Info
### Backbone
- Size: ~ 43 GiB
- Loaded format: bitsandbytes nf4
- Size loaded on GPU: ~ 43 GiB
### LoRA adapters
- rank: 64
- lora_alpha: 16
- target_modules: ["down_proj", "up_proj", "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj"]
## Usage
### Training
Here's an example of loading this model and preparing for the LoRA fine-tuning.
```python
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
MODEL_ID = "LoftQ/Meta-Llama-3-70B-4bit-64rank"
base_model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
peft_model = PeftModel.from_pretrained(
base_model,
MODEL_ID,
subfolder="loftq_init",
is_trainable=True,
)
# Do training with peft_model ...
```
See the full code at our [Github Repo]((https://github.com/yxli2123/LoftQ))
## 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}
}
```