This documentation introduces how to fine-tune large language models into decision models using Unsloth. These models are designed to make decisions with calibrated probabilities rather than simply generating text. Users can train models like Qwen or Gemma to read input, score provided options, and return a specific choice. The process is supported through the Unsloth Desktop app, free notebooks, or custom Python code, and the resulting model can be integrated directly into the Decision API for practical use.
Clef is a 27B multimodal model developed by Cloudflare that transforms state data and schemas of typed questions into structured decisions. By accepting text, JSON, images, or video as input, it returns a probability for every allowed option of each question in a single forward pass, entirely eliminating free-form text generation and output parsing. The model is post-trained from Qwen/Qwen3.8-27B and features a joint schema head that routes evidence to each question and scores all options simultaneously. It is fully compatible with Jev and SystemOne APIs, making it well-suited for deterministic, structured decision-making tasks.
- Supports `choice`, `score`, and `noul` (true/false) question types
- A smaller, faster variant called Clef-Flash is also available
- Achieves strong benchmark results, particularly on BFCL (98.5%), ToolRet (69.2), and API-Bank (91.9%)
- Significantly faster than comparable models, with a median latency of 209.3 ms
- Released under the Apache-2.0 license
- Supports `choice`, `score`, and `noul` (true/false) question types
- A smaller, faster variant called Clef-Flash is also available
- Achieves strong benchmark results, particularly on BFCL (98.5%), ToolRet (69.2), and API-Bank (91.9%)
- Significantly faster than comparable models, with a median latency of 209.3 ms
- Released under the Apache-2.0 license
