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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)
+
+
+Mistral-7B-v0.1-4bit-32rank - GGUF
+- Model creator: https://huggingface.co/LoftQ/
+- Original model: https://huggingface.co/LoftQ/Mistral-7B-v0.1-4bit-32rank/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [Mistral-7B-v0.1-4bit-32rank.Q2_K.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q2_K.gguf) | Q2_K | 2.53GB |
+| [Mistral-7B-v0.1-4bit-32rank.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
+| [Mistral-7B-v0.1-4bit-32rank.IQ3_S.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.IQ3_S.gguf) | IQ3_S | 2.96GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
+| [Mistral-7B-v0.1-4bit-32rank.IQ3_M.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.IQ3_M.gguf) | IQ3_M | 3.06GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q3_K.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q3_K.gguf) | Q3_K | 3.28GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
+| [Mistral-7B-v0.1-4bit-32rank.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q4_0.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q4_0.gguf) | Q4_0 | 3.83GB |
+| [Mistral-7B-v0.1-4bit-32rank.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.IQ4_NL.gguf) | IQ4_NL | 3.87GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q4_K_S.gguf) | Q4_K_S | 3.86GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q4_K.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q4_K.gguf) | Q4_K | 4.07GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q4_K_M.gguf) | Q4_K_M | 4.07GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q4_1.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q4_1.gguf) | Q4_1 | 4.24GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q5_0.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q5_0.gguf) | Q5_0 | 4.65GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q5_K_S.gguf) | Q5_K_S | 4.65GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q5_K.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q5_K.gguf) | Q5_K | 4.78GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q5_K_M.gguf) | Q5_K_M | 4.78GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q5_1.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q5_1.gguf) | Q5_1 | 5.07GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q6_K.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q6_K.gguf) | Q6_K | 5.53GB |
+| [Mistral-7B-v0.1-4bit-32rank.Q8_0.gguf](https://huggingface.co/RichardErkhov/LoftQ_-_Mistral-7B-v0.1-4bit-32rank-gguf/blob/main/Mistral-7B-v0.1-4bit-32rank.Q8_0.gguf) | Q8_0 | 7.17GB |
+
+
+
+
+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, `Mistral-7B-v0.1-4bit-32rank`, is obtained from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). 
+The backbone is under `LoftQ/Mistral-7B-v0.1-4bit-32rank` and LoRA adapters are under the `subfolder='loftq_init'`.
+
+## Model Info
+### Backbone
+- Stored format: `torch.bfloat16`
+- Size: ~ 14 GiB
+- Loaded format: bitsandbytes nf4
+- Size loaded on GPU: ~3.5 GiB
+
+### LoRA adapters
+- rank: 32
+- 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/Mistral-7B-v0.1-4bit-32rank"
+
+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 ...
+```
+
+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}
+}
+```
+