Fine-tuning

Fine-tuners are registered as tools like everything else, but unlike the retrieval and audio tools they are keyed to a base model — the vendor decides which of its models are tunable, and that list moves.

Registered fine-tuners

| Backend | Base models | Credentials | | --- | --- | --- | | Google Vertex AI | Gemini (supervised tuning) | Divinci-managed | | OpenAI | GPT-4o, GPT-4o-mini | BYOK | | Cloudflare Workers AI LoRA | Gemma 3 12B IT, Mistral Small 3.1 24B Instruct | Divinci-managed | | AWS Bedrock — Claude | Claude 3 Haiku | BYOK (AWS) | | AWS Bedrock — Nova | Nova Micro, Lite, Pro, Premier | BYOK (AWS) | | Modal (Unsloth QLoRA) | Gemma 4 27B on H100 | BYOK | | Tinker (Thinking Machines Lab) | Managed LoRA | BYOK |

Cloudflare LoRA jobs train through Hugging Face AutoTrain and serve the resulting adapter on Workers AI, so a LoRA fine-tune touches two vendors even though it is one choice in the UI.

A vendor's tunable-model list is not discoverable from its API

Only a subset of any vendor's catalog can be tuned, the subset changes without notice, and the publisher APIs do not expose which models those are — a retired base model can keep answering inference calls while every tuning job against it fails. If a job fails at submission with an unhelpful error, check the base model is still tunable before debugging your dataset.

BYOK-only backends refuse before they start

On a backend that has no Divinci-managed credential, a workspace with no provider key attached gets FORBIDDEN at job creation — not a queued job that fails later. Attach the key first.

What actually decides the outcome

The backend matters less than the data. A fine-tune reproduces the shape of what you feed it, so the leverage is in training-data generation and flagging — the conversation flaggers that drop turns with no responder, duplicate dates, or bare URLs do more for quality than switching vendors.

Give a fine-tuned release a fallback

A release whose primary assistant is a fine-tune and which has no fallbackAssistants has nothing to fail over to when the fine-tune errors. Set one.

See also

• Fine-tuning & Training Data — the API and workflow.
• Model Catalog