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, `LoftQ/Phi-3-mini-4k-instruct-4bit-64rank`, is obtained from [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct). 
+The backbone is under `LoftQ/Phi-3-mini-4k-instruct-4bit-64rank` and LoRA adapters are under the `subfolder='loftq_init'`.
+
+## Model Info
+### Backbone
+- Stored format: nf4
+- Size: ~ 5.5 GiB
+- Loaded format: bitsandbytes nf4
+- Size loaded on GPU: ~1.4 GiB
+
+### LoRA adapters
+- rank: 64
+- lora_alpha: 16
+- target_modules: ["qkv_proj", "o_proj", "up_gate_proj", "down_proj"]
+- rank_pattern: {"qkv_proj": 192, "up_gate_proj": 128}
+
+## 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-3-mini-4k-instruct-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}
+}
+```