Instructions to use EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml with Transformers:

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

pipe = pipeline("text-generation", model="EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml", trust_remote_code=True)
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 EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml

SGLang

How to use EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml 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 "EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml" \
    --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": "EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml",
		"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 "EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml" \
        --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": "EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml with Docker Model Runner:
```
docker model run hf.co/EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml
```

Mistral-7B-Instruct-v0.3 ONNX models for DirectML

This repository hosts the optimized versions of Mistral-7B-Instruct-v0.3 to accelerate inference with ONNX Runtime for DirectML.

Usage on Windows (Intel / AMD / Nvidia / Qualcomm)

conda create -n onnx python=3.10
conda activate onnx
winget install -e --id GitHub.GitLFS
pip install huggingface-hub[cli]
huggingface-cli download EmbeddedLLM/mistral-7b-instruct-v0.3-int4-onnx-directml --local-dir .\mistral-7b-instruct-v0.3
pip install numpy==1.26.4
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/microsoft/onnxruntime-genai/main/examples/python/phi3-qa.py" -OutFile "phi3-qa.py"
pip install onnxruntime-directml
pip install --pre onnxruntime-genai-directml
conda install conda-forge::vs2015_runtime
python phi3-qa.py -m .\mistral-7b-instruct-v0.3

What is DirectML

DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and Qualcomm.

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