#
optimum-neuron plugin for vLLM

The `optimum-neuron` package includes a vLLM plugin that registers an ‘optimum-neuron’ vLLM platform specifically designed to ease the deployment of models hosted on the Hugging Face hub to AWS Trainium and Inferentia.

This platform supports two modes of operation:

- it can be used for the inference of pre-exported Neuron models directly from the hub,
- but it allows also the simplified deployment of vanilla models directly without recompilation using cached artifacts.

Notes

- only a relevant subset of all possible configurations for a given model are cached,
- you can use the `optimum-cli` to get all cached configurations for each model.
- to deploy models that are not cached on the Hugging Face hub, you need to export them beforehand.

##
Setup

The easiest way to use the `optimum-neuron` vLLM platform is to launch an Amazon ec2 instance using the Hugging Face Neuron Deep Learning AMI. If you decide NOT to make your life easier by using Hugging Face Neuron Deep Learning AMI, you can install this functionality into your Neuron environment with `pip install optimum-neuron[neuronx,vllm]`.

Note: Trn2 instances are not supported by the `optimum-neuron` platform yet.

- After launching the instance, follow the instructions in Connect to your instance to connect to the instance
- Once inside your instance, activate the pre-installed `optimum-neuron` virtual environment by running

    source /opt/aws_neuronx_venv_pytorch_2_7/bin/activate

##
Generating content programmatically

The easiest way to test a model is to use the python API:

    from vllm import LLM, SamplingParams

    prompts = [
        "Hello, my name is",
        "The president of the United States is",
        "The capital of France is",
        "The future of AI is",
    ]
    sampling_params = SamplingParams(temperature=0.8, top_p=0.95)

    llm = LLM(model="unsloth/Llama-3.2-1B-Instruct",
              max_num_seqs=4,
              max_model_len=4096,
              tensor_parallel_size=2)

    outputs = llm.generate(prompts, sampling_params)

    for output in outputs:
        prompt = output.prompt
        generated_text = output.outputs[0].text
        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

##
Serving a model

The easiest way to serve a model is to use the `optimum-cli`:

    optimum-cli neuron serve --model=<model_name_or_path>

The model can be a pre-exported neuron model or a standard hub model.

When deploying a standard hub model, you can customize the way it will be exported:

    optimum-cli neuron serve \
        --model="unsloth/Llama-3.1-1B-Instruct" \
        --batch_size=4 \
        --sequence_length=4096 \
        --tensor_parallel_size=2 \

Note: by default `optimum-cli` will only `serve` standard models for which a cached configuration exists. This behaviour can be overridden using the `--allow_non_cached_model` argument.

If you omit one parameter, `optimum-neuron` will select a default value for you based on the deployment target and prioritizing cached configurations.

Use the following command to test the model:

    curl 127.0.0.1:8080/v1/completions \
        -H 'Content-Type: application/json' \
        -X POST \
        -d '{"prompt":"One of my fondest memory is", "temperature": 0.8, "max_tokens":128}'

##
Custom deployment for advanced users

You can also launch an Open AI compatible inference server directly using vLLM entry points:

    python -m vllm.entrypoints.openai.api_server \
        --model="unsloth/Llama-3.2-1B-Instruct" \
        --max-num-seqs=4 \
        --max-model-len=4096 \
        --tensor-parallel-size=2 \
        --port=8080