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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+phi-2-4bit-64rank - bnb 4bits
+- Model creator: https://huggingface.co/LoftQ/
+- Original model: https://huggingface.co/LoftQ/phi-2-4bit-64rank/
+
+
+
+
+Original model description:
+---
+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}
+}
+```
+
+