Create README.md

README.md

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+---
+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: 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/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}
+}
+```