README.md

---
license: mit
language:
- en
pipeline_tag: text-generation
tags:
- 'quantization '
- lora
---
# 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, `phi-2-4bit-64rank`, is obtained from [phi-2](https://huggingface.co/microsoft/phi-2). 
The backbone is under `LoftQ/phi-2-4bit-64rank` and LoRA adapters are under the `subfolder='loftq_init'`.

## Model Info
### Backbone
- Stored format: `torch.float16`
- Size: ~ 5.5 GiB
- Loaded format: bitsandbytes nf4
- Size loaded on GPU: ~1.4 GiB

### LoRA adapters
- rank: 64
- lora_alpha: 16
- target_modules: ["q_proj", "k_proj", "v_proj", "dense", "fc1", "fc2"]

## 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/phi-2-4bit-64rank"

base_model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, 
    torch_dtype=torch.float32,  # you may change it with different models
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float32,  # float32 is tested and veryfied
        bnb_4bit_use_double_quant=False,
        bnb_4bit_quant_type='nf4',
    ),
)
peft_model = PeftModel.from_pretrained(
    base_model,
    MODEL_ID,
    subfolder="loftq_init",
    is_trainable=True,
)

# Do training with peft_model ...
```

## Experiment Results
We have conducted experiments on supervised fine-tuning of [GSM8K](https://huggingface.co/datasets/gsm8k).

| Model   | Bits | Rank | LoRA Initial           | GSM8K     |
| --------| ---- | ---- | ---------------------- | --------- |
| Phi-2   | 16   | -    | Full model fine-tuning | 66.8±1.2  |
| Phi-2   | 16   | 64   | Gaussian + 0 (LoRA)    | 64.8±0.5  |
| Phi-2   | 4    | 64   | Gaussian + 0 (QLoRA)   | 60.2±0.6  |
| Phi-2   | 4    | 64   | LoftQ                  | 64.1±0.7  |



**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, BitsAndBytesConfig
from peft import PeftModel

MODEL_ID = "LoftQ/phi-2-4bit-64rank"

base_model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, 
    torch_dtype=torch.float32,  # you may change it with different models
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.float32,  # float32 is tested and veryfied
        bnb_4bit_use_double_quant=False,
        bnb_4bit_quant_type='nf4',
    ),
)
peft_model = PeftModel.from_pretrained(
    base_model,
    MODEL_ID,
    subfolder="gsm8k",
    is_trainable=True,
)

# Do inference 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}
}
```