Use the model API to upload your model and run inference on a dedicated endpoint

Requirements

Currently, we support models that meet the following criteria.

Source: We support uploads from from Hugging Face or S3.

Type: We support text generation models

Parameters: Models must have parameter-count of 300 billion or less

Base models: Uploads currently work with the following base models

deepseek-ai/DeepSeek-R1-Distill-Llama-70B
google/gemma-2-27b-it
meta-llama/Llama-3.3-70B-Instruct-Turbo
meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo
meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo
meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo
meta-llama/Llama-3-8b-chat-hf
meta-llama/Llama-2-70b-hf
meta-llama/LlamaGuard-2-8b
mistralai/Mistral-7B-Instruct-v0.3
mistralai/Mixtral-8x7B-Instruct-v0.1
Qwen/Qwen2.5-72B-Instruct-Turbo
Qwen/Qwen2-VL-72B-Instruct
Qwen/Qwen2-72B-Instruct
Salesforce/Llama-Rank-V1

Getting Started

Upload the model

Currently, model uploads can be done via the API or the Together web interface.

Web Interface

To upload via the web, just log in and navigate to models > add custom model to reach this page:

Then fill in the source URL (S3 or Hugging Face), the model name and how you would like it described in your Together account once uploaded.

API

S3

To upload a model from S3, list your model name and provide a presigned URL

curl -X POST "https://api.together.xyz/v1/models" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "Qwen/Qwen2-72B-Instruct",
    "model_source": "https://ml-models.s3.us-west-2.amazonaws.com/models/2023/model.tar.gz",
    "description": "Finetuned Qwen/Qwen2-72B-Instruct uploaded from my S3 bucket",
  }'

Hugging Face

To upload model from Hugging Face, list your model name and Hugging Face token

curl -X POST "https://api.together.xyz/v1/models" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model_name": "Qwen2.5-72B-Instruct",
    "model_source": "unsloth/Qwen2.5-72B-Instruct",
    "hf_token": "hf_examplehuggingfacetoken",
    "description": "Finetuned Qwen2.5-72B-Instruct by Unsloth"
  }'

Response

{
    "data": {
        "job_id": "job-a15dad11-8d8e-4007-97c5-a211304de284",
        "model_name": "necolinehubner/Qwen2.5-72B-Instruct",
        "model_id": "model-c0e32dfc-637e-47b2-bf4e-e9b2e58c9da7",
        "model_source": "huggingface"
    },
    "message": "Processing model weights. Job created."
}

You can check the status of the job

curl -X GET "https://api.together.xyz/v1/jobs/job-a15dad11-8d8e-4007-97c5-a211304de284" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \

Response

{
    "type": "model_upload",
    "job_id": "job-a15dad11-8d8e-4007-97c5-a211304de284",
    "status": "Complete",
    "status_updates": [
        {
            "status": "Queued",
            "message": "Job has been created",
            "timestamp": "2025-03-11T22:05:43Z"
        },
        {
            "status": "Running",
            "message": "Received job from queue, starting",
            "timestamp": "2025-03-11T22:06:10Z"
        },
        {
            "status": "Running",
            "message": "Model download in progress",
            "timestamp": "2025-03-11T22:06:10Z"
        },
        {
            "status": "Running",
            "message": "Model validation in progress",
            "timestamp": "2025-03-11T22:15:23Z"
        },
        {
            "status": "Running",
            "message": "Model upload in progress",
            "timestamp": "2025-03-11T22:16:41Z"
        },
        {
            "status": "Complete",
            "message": "Job is Complete",
            "timestamp": "2025-03-11T22:36:12Z"
        }
    ],
    "args": {
        "description": "Finetuned Qwen2.5-72B-Instruct by Unsloth",
        "modelName": "necolinehubner/Qwen2.5-72B-Instruct",
        "modelSource": "unsloth/Qwen2.5-72B-Instruct"
    },
    "created_at": "2025-03-11T22:05:43Z",
    "updated_at": "2025-03-11T22:36:12Z"
}

Deploy the model

Uploaded models are treated like any other dedicated endpoint models.
Deploying a custom model can be done via the CLI, API or the UI

Deploying custom model on the UI

All models, custom and finetuned models as well as any model that has a dedicated endpoint will be listed under My Models. To deploy a custom model

Select the model to open the model page

The model page will display details from your uploaded model with an option to create a dedicated endpoint

When you select 'Create Dedicated Endpoint' you will see an option to configure the deployment

Once an endpoint has been deployed, you can interact with it on the playground or via the API