#
Together

> All supported Together models can be found here

Together decentralized cloud services empower developers and researchers at organizations of all sizes to train, fine-tune, and deploy generative AI models.

##
Supported tasks

###
Chat Completion (LLM)

Find out more about Chat Completion (LLM) here.

Language

Client

Provider

Settings

    import os
    from openai import OpenAI

    client = OpenAI(
        base_url="https://router.huggingface.co/v1",
        api_key=os.environ["HF_TOKEN"],
    )

    completion = client.chat.completions.create(
        model="Qwen/Qwen3-Next-80B-A3B-Instruct:together",
        messages=[
            {
                "role": "user",
                "content": "What is the capital of France?"
            }
        ],
    )

    print(completion.choices[0].message)

###
Chat Completion (VLM)

Find out more about Chat Completion (VLM) here.

Language

Client

Provider

Settings

    import os
    from openai import OpenAI

    client = OpenAI(
        base_url="https://router.huggingface.co/v1",
        api_key=os.environ["HF_TOKEN"],
    )

    completion = client.chat.completions.create(
        model="meta-llama/Llama-4-Scout-17B-16E-Instruct:together",
        messages=[
            {
                "role": "user",
                "content": [
                    {
                        "type": "text",
                        "text": "Describe this image in one sentence."
                    },
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
                        }
                    }
                ]
            }
        ],
    )

    print(completion.choices[0].message)

###
Text To Image

Find out more about Text To Image here.

Language

Provider

Settings

    import os
    from huggingface_hub import InferenceClient

    client = InferenceClient(
        provider="together",
        api_key=os.environ["HF_TOKEN"],
    )

    # output is a PIL.Image object
    image = client.text_to_image(
        "Astronaut riding a horse",
        model="black-forest-labs/FLUX.1-dev",
    )

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