Instructions to use OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov with Transformers:

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

pipe = pipeline("text-generation", model="OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov")
model = AutoModelForCausalLM.from_pretrained("OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov

SGLang

How to use OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-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/Mixtral-8x7B-Instruct-v0.1-int8-ov" \
    --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": "OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov",
		"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 "OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov" \
        --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": "OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov with Docker Model Runner:
```
docker model run hf.co/OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov
```

Mixtral-8x7B-Instruct-v0.1-int8-ov / README.md

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	---
	license: apache-2.0
	language:
	- en
	base_model:
	- mistralai/Mixtral-8x7B-Instruct-v0.1
	base_model_relation: quantized
	---

	# Mixtral-8x7b-Instruct-v0.1-int8-ov

	* Model creator: [Mistral AI](https://huggingface.co/mistralai)
	* Original model: [Mixtral 8X7B Instruct v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)

	## Description

	This is [Mixtral-8x7b-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).

	## Quantization Parameters

	Weight compression was performed using `nncf.compress_weights` with the following parameters:

	* mode: INT8_ASYM

	For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html).

	## Compatibility

	The provided OpenVINO™ IR model is compatible with:

	* OpenVINO version 2024.2.0 and higher
	* Optimum Intel 1.17.0 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:

	```
	pip install optimum[openvino]
	```

	2. Run model inference:

	```
	from transformers import AutoTokenizer
	from optimum.intel.openvino import OVModelForCausalLM

	model_id = "OpenVINO/mixtral-8x7b-instruct-v0.1-int8-ov"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = OVModelForCausalLM.from_pretrained(model_id)

	inputs = tokenizer("What is OpenVINO?", return_tensors="pt")

	outputs = model.generate(**inputs, max_length=200)
	text = tokenizer.batch_decode(outputs)[0]
	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).

	## 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/mixtral-8x7b-instruct-v0.1-int8-ov"
	model_path = "mixtral-8x7b-instruct-v0.1-int8-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?", max_length=200))
	```

	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/Mixtral-8x7B-Instruct-v0.1#limitations).

	## Legal information

	The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).

	## Disclaimer

	Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.