license: apache-2.0
Model Details
This model is an int4 model with group_size 64 of tiiuae/falcon-7b generated by intel/auto-round, because there is an issue when evaluating with group_size 128.
How To Use
INT4 Inference with ITREX on CPU
Install the latest intel-extension-for-transformers
from intel_extension_for_transformers.transformers import AutoModelForCausalLM
from transformers import AutoTokenizer
quantized_model_dir = "Intel/falcon-7b-int4-inc"
model = AutoModelForCausalLM.from_pretrained(quantized_model_dir,
device_map="auto",
trust_remote_code=False,
use_neural_speed=False,
)
tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir, use_fast=True)
print(tokenizer.decode(model.generate(**tokenizer("There is a girl who likes adventure,", return_tensors="pt").to(model.device),max_new_tokens=50)[0]))
"""
There is a girl who likes adventure, and she is a girl who likes to be a hero. She is a girl who likes to be a hero. She is a girl who likes to be a hero. She is a girl who likes to be a hero. She is a girl who
"""
INT4 Inference with AutoGPTQ
##pip install auto-gptq
from auto_gptq import AutoGPTQForCausalLM
from transformers import AutoTokenizer
quantized_model_dir = "Intel/falcon-7b-int4-inc"
tokenizer = AutoTokenizer.from_pretrained(quantized_model_dir)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
text = "There is a girl who likes adventure,"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50, do_sample=False)[0]))
#There is a girl who likes adventure, and she is a girl who likes to travel. She is a girl who likes to go to the beach, and she is a girl who likes to go to the mountains. She is a girl who likes to go to the city, and she is
Evaluate the model
pip3 install lm-eval==0.4.2
lm_eval --model hf --model_args pretrained="Intel/falcon-7b-int4-inc",autogptq=True,gptq_use_triton=True --device cuda:0 --tasks lambada_openai,hellaswag,piqa,winogrande,truthfulqa_mc1,openbookqa,boolq,arc_easy,arc_challenge,mmlu --batch_size 16
| Metric | BF16 | int4 |
|---|---|---|
| Avg. | 0.5462 | 0.5454 |
| mmlu | 0.2546 | 0.2562 |
| lambada_openai | 0.7450 | 0.7485 |
| hellaswag | 0.5773 | 0.5719 |
| winogrande | 0.6740 | 0.6835 |
| piqa | 0.7943 | 0.7905 |
| truthfulqa_mc1 | 0.2228 | 0.2166 |
| openbookqa | 0.3080 | 0.3100 |
| boolq | 0.7361 | 0.7431 |
| arc_easy | 0.7475 | 0.7424 |
| arc_challenge | 0.4027 | 0.3908 |
Reproduce the model
Here is the sample command to reproduce the model
git clone https://github.com/intel/auto-round
cd auto-round/examples/language-modeling
pip install -r requirements.txt
python3 main.py \
--model_name tiiuae/falcon-7b \
--device 0 \
--group_size 64 \
--bits 4 \
--iters 1000 \
--disable_quanted_input \
--deployment_device 'gpu' \
--output_dir "./tmp_autoround"
Ethical Considerations and Limitations
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of the model, developers should perform safety testing.
Caveats and Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
Cite
@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }