Upload a custom model or adapter

# Docs for v1 can be found by changing the above selector ^ from together import Together import os client = Together( api_key=os.environ.get("TOGETHER_API_KEY"), ) response = client.models.upload( model_name="My-Fine-Tuned-Model", model_source="https://ml-models.s3.us-west-2.amazonaws.com/models/my-fine-tuned-model.tar.gz", ) print(response.data.job_id)

{ "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." }

Authorizations

Authorization

string

header

default:default

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

model_name

string

required

The name to give to your uploaded model

Example:

"Qwen2.5-72B-Instruct"

model_source

string

required

The source location of the model (Hugging Face repo or S3 path)

Example:

"unsloth/Qwen2.5-72B-Instruct"

model_type

enum<string>

default:model

Whether the model is a full model or an adapter

Available options:

model,

adapter

Example:

"model"

hf_token

string

Hugging Face token (if uploading from Hugging Face)

Example:

"hf_examplehuggingfacetoken"

description

string

A description of your model

Example:

"Finetuned Qwen2.5-72B-Instruct by Unsloth"

base_model

string

The base model to use for an adapter if setting it to run against a serverless pool. Only used for model_type adapter.

Example:

"Qwen/Qwen2.5-72B-Instruct"

lora_model

string

The lora pool to use for an adapter if setting it to run against, say, a dedicated pool. Only used for model_type adapter.

Example:

"my_username/Qwen2.5-72B-Instruct-lora"

Response

200 - application/json

Model / adapter upload job created successfully

data

object

required

Show child attributes

message

string

required

Example:

"Processing model weights. Job created."