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+ ---
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+ license: mit
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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+ tags:
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+ - 'quantization '
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+ - lora
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+ - loftq
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+ - llama
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+ ---
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+ # LoftQ Initialization
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+
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+ | [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) |
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+
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+ 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.
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+
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+ This model, `LoftQ/Meta-Llama-3-70B-Instruct-4bit-64rank`, is obtained from [Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
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+ The backbone is under `LoftQ/Meta-Llama-3-70B-Instruct-4bit-64rank` and LoRA adapters are under the `subfolder='loftq_init'`.
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+
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+ ## Model Info
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+ ### Backbone
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+ - Size: ~ 46 GiB
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+ - Loaded format: bitsandbytes nf4
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+ - Size loaded on GPU: ~ 46 GiB
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+
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+ ### LoRA adapters
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+ - rank: 64
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+ - lora_alpha: 16
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+ - target_modules: ["down_proj", "up_proj", "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj"]
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+
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+ ## Usage
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+
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+ ### Training
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+ Here's an example of loading this model and preparing for the LoRA fine-tuning.
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+
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+ ```python
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+ import torch
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+ from transformers import AutoModelForCausalLM, BitsAndBytesConfig
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+ from peft import PeftModel
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+
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+ MODEL_ID = "LoftQ/Meta-Llama-3-70B-Instruct-4bit-64rank"
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+
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+ base_model = AutoModelForCausalLM.from_pretrained(MODEL_ID)
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+ peft_model = PeftModel.from_pretrained(
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+ base_model,
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+ MODEL_ID,
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+ subfolder="loftq_init",
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+ is_trainable=True,
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+ )
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+
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+ # Do training with peft_model ...
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+ ```
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+
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+ See the full code at our [Github Repo]((https://github.com/yxli2123/LoftQ))
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+
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+
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+ ## Citation
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+
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+ ```bibtex
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+ @article{li2023loftq,
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+ title={Loftq: Lora-fine-tuning-aware quantization for large language models},
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+ author={Li, Yixiao and Yu, Yifan and Liang, Chen and He, Pengcheng and Karampatziakis, Nikos and Chen, Weizhu and Zhao, Tuo},
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+ journal={arXiv preprint arXiv:2310.08659},
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+ year={2023}
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+ }
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+ ```