RichardErkhov/LoftQ_-_phi-2-4bit-64rank-gguf
0193
Quantization made by Richard Erkhov.
phi-2-4bit-64rank - GGUF
- 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 | 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, phi-2-4bit-64rank, is obtained from 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
- targetmodules: ["qproj", "kproj", "vproj", "dense", "fc1", "fc2"]
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-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.
Inference Here is an example code for inference after the model has been fine-tuned on GSM8K.
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)
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}
}