How to Fine-Tune an Open LLM on Together AI
Fine-tuning on Together AI lets you take an open base model and adapt it to your data without managing a training cluster. You upload a dataset, pick a base model, and the platform runs the job and hands back a model endpoint. It is the middle ground between prompting and full custom training.
Do this after you can already call models through the API. The fine-tuning flow builds on the same account and keys.
When is fine-tuning worth it?
Fine-tune when you need a consistent output format, a specific tone, or domain knowledge that is too large to fit in a prompt. If a good prompt already solves the task, prompting is cheaper and faster to iterate. Fine-tuning pays off at volume, where per-call prompt length starts costing more than a tuned model would.
How do you prepare the data?
Use JSONL with one example per line. For chat models, each line holds a messages array with system, user and assistant turns.
- Clean your examples — remove duplicates and bad outputs.
- Format as JSONL, one conversation per line.
- Aim for a few hundred to a few thousand high-quality rows, not a huge noisy set.
- Split off a small validation slice.
- Upload the file through the API or dashboard.
Quality beats quantity. 500 clean examples usually outperform 5,000 messy ones.
How do you launch and monitor a job?
Create a fine-tuning job with the base model ID and your file ID, then poll the job status until it completes. The platform reports loss over training steps so you can spot a model that is not learning. Training a modest model on a few thousand rows typically finishes within hours, not days.
How do you use the tuned model?
Once the job finishes, you get a new model ID you can call through the same API, at a per-token price that depends on the base model size. For consistent production traffic, attach a dedicated endpoint so capacity is reserved and latency stays stable. Test the tuned model against your prompt-only baseline before switching production over.
What does it cost, and what are the limits?
You pay for training time plus storage, then per-token inference on the tuned model. The costs are modest compared with renting GPUs yourself. Two caveats: you cannot fine-tune every model in the catalog, and a tuned model can get worse at general tasks as it specializes — keep the base model around for everything else.
Key Takeaways
- Together AI fine-tuning takes a JSONL dataset and returns a callable model.
- Format chat data as one messages array per line.
- Hundreds of clean examples often beat thousands of noisy ones.
- You pay for training, storage, then per-token inference.
- Not all models are tunable; a tuned model may lose general ability.
Last updated October 2026. More at Together AI.
