Phi-3-mini-4k-instruct-4bit-64rank

LoftQ Initialization

| Paper | Code | PEFT Example |

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. 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: ~ 2.5 GiB
• Loaded format: bitsandbytes nf4
• Size loaded on GPU: ~2.5 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.

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, trust_remote_code=True)
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

Citation

@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}
}