Instructions to use INC4AI/Mistral-7B-v0.1-int4-inc-lmhead with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use INC4AI/Mistral-7B-v0.1-int4-inc-lmhead with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="INC4AI/Mistral-7B-v0.1-int4-inc-lmhead")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("INC4AI/Mistral-7B-v0.1-int4-inc-lmhead")
model = AutoModelForCausalLM.from_pretrained("INC4AI/Mistral-7B-v0.1-int4-inc-lmhead", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use INC4AI/Mistral-7B-v0.1-int4-inc-lmhead with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "INC4AI/Mistral-7B-v0.1-int4-inc-lmhead"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "INC4AI/Mistral-7B-v0.1-int4-inc-lmhead",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/INC4AI/Mistral-7B-v0.1-int4-inc-lmhead

SGLang

How to use INC4AI/Mistral-7B-v0.1-int4-inc-lmhead with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "INC4AI/Mistral-7B-v0.1-int4-inc-lmhead" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "INC4AI/Mistral-7B-v0.1-int4-inc-lmhead",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "INC4AI/Mistral-7B-v0.1-int4-inc-lmhead" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "INC4AI/Mistral-7B-v0.1-int4-inc-lmhead",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use INC4AI/Mistral-7B-v0.1-int4-inc-lmhead with Docker Model Runner:
```
docker model run hf.co/INC4AI/Mistral-7B-v0.1-int4-inc-lmhead
```

Model Details: Mistral-7B-v0.1-int4-inc-lmhead

This model is an int4 model with group_size 128 and quantized lmhead of mistralai/Mistral-7B-v0.1 generated by intel/auto-round.

How To Use

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  mistralai/Mistral-7B-v0.1 \
--device 0 \
--group_size 128 \
--bits 4 \
--iters 1000 \
--quant_lm_head \
--disable_low_gpu_mem_usage \
--deployment_device 'gpu' \
--output_dir "./tmp_autoround"

Use the model

pip install auto-gptq

Install auto-round from source first

from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_round.auto_quantizer import AutoHfQuantizer 
quantized_model_dir = "Intel/Mistral-7B-v0.1-int4-inc-lmhead"
model = AutoModelForCausalLM.from_pretrained(quantized_model_dir,
                                             device_map="auto",
                                             trust_remote_code=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]))

Evaluate the model

pip install lm-eval==0.4.2

git clone https://github.com/intel/auto-round
cd auto-round/examples/language-modeling
python3 eval_042/evluation.py --model_name "Intel/Mistral-7B-v0.1-int4-inc-lmhead" --eval_bs 32

Metric	BF16	INT4-lmhead	INT4
Avg.	0.6260	0.6228	0.6218
mmlu	0.5868	0.5760	0.5772
lambada_openai	0.7555	0.7539	0.7543
hellaswag	0.6125	0.6055	0.6072
winogrande	0.7395	0.7380	0.7388
piqa	0.8069	0.8009	0.8030
truthfulqa_mc1	0.2803	0.2876	0.2864
openbookqa	0.3280	0.3300	0.3260
boolq	0.8379	0.8291	0.8281
arc_easy	0.8089	0.8043	0.8035
arc_challenge	0.5034	0.5026	0.4932

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:

Intel Neural Compressor link
Intel Extension for Transformers link

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.

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Safetensors

Model size

1B params

Tensor type

I32

BF16

INC4AI
/

Mistral-7B-v0.1-int4-inc-lmhead