---
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, `Llama-2-13b-hf-4bit-64rank`, is obtained from [LLAMA-2-13b](https://huggingface.co/meta-llama/Llama-2-13b-hf). 
The backbone is under `LoftQ/Llama-2-13b-hf-4bit-64rank` and LoRA adapters are under the `subfolder='loftq_init'`.

## Model Info
### Backbone
- Stored format: `torch.bfloat16`
- Size: ~ 26 GiB
- Loaded format: bitsandbytes nf4
- Size loaded on GPU: ~6.5 GiB

### LoRA adapters
- rank: 64
- lora_alpha: 64
- 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/Llama-2-13b-hf-4bit-64rank"

base_model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, 
    torch_dtype=torch.bfloat16,  # you may change it with different models
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,  # bfloat16 is recommended
        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) 
and [WikiText-2](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1).

| Model          | Bits | Rank | LoRA Initial         | GSM8K | WikiText-2 |
| -------------- | ---- | ---- | -------------------- | ----- | ---------- |
| LLAMA-2-13b     | 16   | 64   | Gaussian + 0         | 45.3  | 5.12       |
| LLAMA-2-13b     | 4    | 64   | Gaussian + 0 (QLoRA) | 39.9  | 5.22       |
| **LLAMA-2-13b** | 4    | 64   | LoftQ                | 45.0  | 5.16       |



**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/Llama-2-13b-hf-4bit-64rank"

base_model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, 
    torch_dtype=torch.bfloat16,  # you may change it with different models
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_compute_dtype=torch.bfloat16,  # bfloat16 is recommended
        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}
}
```