Get comfortable with how Together AI serves and fine-tunes open models.
0 / 5 completed
1 / 5
At standup, the team wants to serve an open-weight Llama model via API. What does Together AI primarily offer?
Together AI specializes in open model serving, hosting many open-weight models behind an OpenAI-compatible API. You call hosted inference endpoints without managing GPUs yourself. This is its core value proposition for teams using open models.
2 / 5
During a design review, you want to specialize a base model on your own data. Which Together capability fits?
Together AI exposes a fine-tuning API that lets you train a base open model on uploaded datasets and then serve the result. After the job completes you receive a model id usable on its inference endpoints. This closes the loop from customization to deployment.
3 / 5
In a code review, a dev calls a single hosted URL with a model field. What is this called on Together?
An inference endpoint on Together AI is the hosted API surface you POST to, selecting the model via the model field. It abstracts away GPU provisioning and scaling. This is how you run open model serving without infrastructure work.
4 / 5
An incident report shows a fine-tune job failed validation. What is the most likely first thing to check?
Fine-tuning failures most often trace to dataset formatting issues, such as malformed JSONL or wrong field names. The fine-tuning API expects a specific schema for examples. Validating the dataset locally before upload prevents wasted job time.
5 / 5
During a PR review, someone migrates from OpenAI to Together. What eases this migration?
Together AI's inference endpoints are OpenAI-compatible, so existing client code often works by changing the base URL and key. This lowers the cost of adopting open model serving. You mainly swap the model id to a Together-hosted one.