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, Meta-Llama-3-70B-4bit-64rank-1iter
, is obtained from LLAMA-3-70B.
The backbone is under LoftQ/Meta-Llama-3-70B-4bit-64rank-1iter
and LoRA adapters are under the subfolder='loftq_init'
.
Model Info
Backbone
- Size: ~ 43 GiB
- Loaded format: bitsandbytes nf4
- Size loaded on GPU: ~43 GiB
LoRA adapters
- rank: 64
- 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.
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
MODEL_ID = "LoftQ/Meta-Llama-3-70B-4bit-64rank-1iter"
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 ...
Inference. Here is an example code for inference after the model has been fine-tuned.
import torch
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
MODEL_ID = "LoftQ/Meta-Llama-3-70B-4bit-64rank-1iter"
ADAPTER_PATH = "you/adapter/path"
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,
ADAPTER_PATH,
)
# Do inference 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}
}
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