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Fine-tuning tailors a pretrained model to a smaller, targeted dataset so it performs better on a specific task or domain. Together AI handles the full lifecycle: data upload, training, hosting, and inference on a dedicated endpoint. Together AI currently supports two fine-tuning approaches:
  • LoRA: Trains a small set of adapter weights on top of the frozen base model. This is the default training mode, as it’s faster, cheaper, and the right choice for most use cases.
  • Full fine-tuning: Updates every weight in the base model. Uses more compute, but can outperform LoRA when the base behavior needs to shift substantially.
See LoRA vs. full fine-tuning to choose between them. This choice is separate from the training method you pick below.

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Get started

Quickstart

Prepare your data, launch a LoRA job on Qwen3 8B, and evaluate the result.

Supported models

Browse every base model you can fine-tune, with context lengths and batch sizes.

Pricing

See how Together AI bills for training tokens and dedicated hosting.

Bring your own model

Fine-tune a model from the Hugging Face Hub that isn’t in the Together catalog.

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Prepare your data

Your training dataset needs to be a JSONL or Parquet file, formatted to match your task. See the data preparation guide for the schemas, validation rules, and example datasets. 
from together import Together

client = Together()

train_file = client.files.upload(
    file="train.jsonl",
    purpose="fine-tune",
    check=True,
)
print(train_file.id)

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Training methods

Supervised fine-tuning

Train on demonstration data with one target completion per example. The default method.

Preference fine-tuning

Align a model with rankings over preferred and dispreferred responses using DPO.

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Advanced guides

Vision fine-tuning

Fine-tune vision-language models on samples with image and text data.

Function calling

Train a model to invoke tools and structured functions reliably.

Reasoning fine-tuning

Train a reasoning model with chain-of-thought data.

LoRA vs. full fine-tuning

Choose how much of the model to update, and tune LoRA’s rank and target modules.

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Monitor and deploy

Monitor a job

Poll job status, then retrieve per-step loss and evaluation metrics.

Deploy your model

Serve your fine-tuned model on a dedicated endpoint or download it for local use.