Instructions to use aws-neuron/Mistral-neuron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aws-neuron/Mistral-neuron with Transformers:

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

pipe = pipeline("text-generation", model="aws-neuron/Mistral-neuron")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aws-neuron/Mistral-neuron")
model = AutoModelForCausalLM.from_pretrained("aws-neuron/Mistral-neuron")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aws-neuron/Mistral-neuron with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/aws-neuron/Mistral-neuron

SGLang

How to use aws-neuron/Mistral-neuron 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 "aws-neuron/Mistral-neuron" \
    --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": "aws-neuron/Mistral-neuron",
		"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 "aws-neuron/Mistral-neuron" \
        --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": "aws-neuron/Mistral-neuron",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aws-neuron/Mistral-neuron with Docker Model Runner:
```
docker model run hf.co/aws-neuron/Mistral-neuron
```

jburtoft commited on Jan 4, 2024

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14d9009

1 Parent(s): dd0f01e

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@@ -3,7 +3,7 @@ license: apache-2.0
 ---
 # Neuronx model for Mistral
-This repository contains [AWS Inferentia2](https://aws.amazon.com/ec2/instance-types/inf2/) and [`neuronx`](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/) compatible checkpoints for[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
 However, this file includes an example of how to compile various versions of Mistral.  Support isn’t available yet (as of 1/3/2024) in the optimum-neuron framework, so we use the base transformers library.

 ---
 # Neuronx model for Mistral
+This repository contains [AWS Inferentia2](https://aws.amazon.com/ec2/instance-types/inf2/) and [`neuronx`](https://awsdocs-neuron.readthedocs-hosted.com/en/latest/) compatible checkpoints for [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).
 However, this file includes an example of how to compile various versions of Mistral.  Support isn’t available yet (as of 1/3/2024) in the optimum-neuron framework, so we use the base transformers library.