Instructions to use jojo1899/Mistral-7B-Instruct-v0.2-ov-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jojo1899/Mistral-7B-Instruct-v0.2-ov-int4 with Transformers:

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

pipe = pipeline("text-generation", model="jojo1899/Mistral-7B-Instruct-v0.2-ov-int4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jojo1899/Mistral-7B-Instruct-v0.2-ov-int4")
model = AutoModelForCausalLM.from_pretrained("jojo1899/Mistral-7B-Instruct-v0.2-ov-int4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jojo1899/Mistral-7B-Instruct-v0.2-ov-int4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jojo1899/Mistral-7B-Instruct-v0.2-ov-int4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jojo1899/Mistral-7B-Instruct-v0.2-ov-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jojo1899/Mistral-7B-Instruct-v0.2-ov-int4

SGLang

How to use jojo1899/Mistral-7B-Instruct-v0.2-ov-int4 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 "jojo1899/Mistral-7B-Instruct-v0.2-ov-int4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jojo1899/Mistral-7B-Instruct-v0.2-ov-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "jojo1899/Mistral-7B-Instruct-v0.2-ov-int4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jojo1899/Mistral-7B-Instruct-v0.2-ov-int4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jojo1899/Mistral-7B-Instruct-v0.2-ov-int4 with Docker Model Runner:
```
docker model run hf.co/jojo1899/Mistral-7B-Instruct-v0.2-ov-int4
```

This is an INT4 quantized version of the mistralai/Mistral-7B-Instruct-v0.2 model. The Python packages used in creating this model are as follows:

openvino==2024.5.0rc1
optimum==1.23.3
optimum-intel==1.20.1
nncf==2.13.0
torch==2.5.1
transformers==4.46.2

This quantized model is created using the following command:

optimum-cli export openvino --model "mistralai/Mistral-7B-Instruct-v0.2" --weight-format int4 --group-size 128 --sym --ratio 1 --all-layers ./Mistral-7B-Instruct-v0.2-ov-int4

For more details, run the following command from your Python environment: optimum-cli export openvino --help

INFO:nncf:Statistics of the bitwidth distribution:

Num bits (N)	% all parameters (layers)	% ratio-defining parameters (layers)
4	100% (226 / 226)	100% (226 / 226)

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OpenVINO models

Collection

A collection of quantized LLMs in OpenVINO format • 5 items • Updated Nov 7, 2024