Instructions to use OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov with Transformers:

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

pipe = pipeline("text-generation", model="OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov")
model = AutoModelForCausalLM.from_pretrained("OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov")
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 OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov

SGLang

How to use OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov 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 "OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov" \
    --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": "OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov",
		"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 "OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov" \
        --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": "OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov with Docker Model Runner:
```
docker model run hf.co/OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov
```

katuni4ka commited on Jul 18, 2024

Commit

64bb66f

verified ·

1 Parent(s): 27cfefa

Update README.md

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README.md +33 -1

README.md CHANGED Viewed

@@ -17,7 +17,8 @@ The provided OpenVINO™ IR model is compatible with:
 * OpenVINO version 2024.1.0 and higher
 * Optimum Intel 1.16.0 and higher
-## Running Model Inference
 1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
@@ -44,6 +45,37 @@ print(text)
 For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
 ## Limitations
 Check the original model card for [limitations](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#limitations).

 * OpenVINO version 2024.1.0 and higher
 * Optimum Intel 1.16.0 and higher
+## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index)
 1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
 For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
+## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai)
+1. Install packages required for using OpenVINO GenAI.
+```
+pip install openvino-genai huggingface_hub
+```
+2. Download model from HuggingFace Hub
+```
+import huggingface_hub as hf_hub
+model_id = "OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov"
+model_path = "Mistral-7B-Instruct-v0.2-fp16-ov"
+hf_hub.snapshot_download(model_id, local_dir=model_path)
+```
+3. Run model inference:
+```
+import openvino_genai as ov_genai
+device = "CPU"
+pipe = ov_genai.LLMPipeline(model_path, device)
+print(pipe.generate("What is OpenVINO?"))
+```
+More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples)
 ## Limitations
 Check the original model card for [limitations](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2#limitations).