Supported models

Every training run names two models. Only the values on this page work: a student_model_name or teacher_model_name from outside this catalogue fails validation rather than falling back to a default.

• Student. The small model we fine-tune for your task. This is the one you deploy and run inference with, so pick a size that fits your latency, cost and hardware constraints.
• Teacher. The large model that generates and validates your synthetic training data. Its knowledge is distilled into the student, and you never deploy it.

Student models

Model	Value
Llama 3.2 1B Instruct	Llama-3.2-1B-Instruct
Llama 3.2 3B Instruct	Llama-3.2-3B-Instruct
Llama 3.1 8B Instruct	Llama-3.1-8B-Instruct
SmolLM2 135M	SmolLM2-135M-Instruct
SmolLM2 1.7B	SmolLM2-1.7B-Instruct
FunctionGemma 270M	functiongemma-270m-it
Gemma 3 270M	gemma-3-270m-it
Gemma 3 1B	gemma-3-1b-it
Gemma 3 4B	gemma-3-4b-it
Gemma 4 E2B	gemma-4-E2B-it
Gemma 4 E4B	gemma-4-E4B-it
Qwen3 0.6B	Qwen3-0.6B
Qwen3 1.7B	Qwen3-1.7B
Qwen3 4B	Qwen3-4B-Instruct-2507
Qwen3 8B	Qwen3-8B
Qwen3.5 0.8B	Qwen3.5-0.8B
Qwen3.5 2B	Qwen3.5-2B
Qwen3.5 4B	Qwen3.5-4B
Qwen3.5 9B	Qwen3.5-9B
Liquid LFM2 350M	LFM2-350M
Liquid LFM2 1.2B	LFM2-1.2B
Liquid LFM2 2.6B	LFM2-2.6B
Liquid LFM2.5 350M	LFM2.5-350M
Liquid LFM2.5 1.2B Instruct	LFM2.5-1.2B-Instruct

Start with Qwen3-1.7B, read the metrics, and only move up in size if the results fall short. Students under 1B parameters give the fastest and cheapest inference and are the easiest to run on-device, but need more training data to reach a given quality bar. Students of 3B and up usually reach higher quality with less work, at slower and more expensive inference.

Trying several is cheap. A sweep is one submission per student against the same training dataset, so it costs training credits only. See Training several students at once.

Teacher models

Model	Value	Tool calling
GPT OSS 120B	openai.gpt-oss-120b	✓
GPT OSS 120B Thinking	openai.gpt-oss-120b-thinking	✓
GPT OSS 20B	openai.gpt-oss-20b	✓
GPT OSS 20B Thinking	openai.gpt-oss-20b-thinking	✓
DeepSeek R1	deepseek.r1	✗
DeepSeek R1 Thinking	deepseek.r1-thinking	✗
DeepSeek V3.1	deepseek.v3.1	✗
DeepSeek V3.2	deepseek.v3.2	✓
Qwen3 235B A22B	Qwen3-235B-A22B-Instruct-2507	✓
Qwen3 480B A35B Coder	Qwen3-480B-A35B-Coder	✗
Qwen2.5 VL 72B	Qwen2.5-VL-72B-Instruct	✗
ZAI GLM 5	zai.glm-5	✓
Moonshot Kimi K2 Thinking	moonshotai.kimi-k2-thinking	✓
Moonshot Kimi K2.5	moonshotai.kimi-k2.5	✓
MiniMax M2 Thinking	minimax.minimax-m2-thinking	✓

The tool calling column covers both tool-calling-closed-book and multi-turn-tool-calling-closed-book.

The default teacher, openai.gpt-oss-120b, is a strong choice for every task type. The teacher is also the main lever when teacher evaluation comes back short, so this is the table to come back to.

Task compatibility

Not every model works with every task type.

• Tool-calling students. Only the Qwen3, Qwen3.5, Llama 3, LFM2, LFM2.5, FunctionGemma and Gemma 4 models handle tool-calling-closed-book and multi-turn-tool-calling-closed-book.
• Tool-calling teachers. Only the ones marked ✓ above.
• Reasoning teachers. The GPT OSS, DeepSeek, GLM, Kimi and MiniMax families need synthgen.teacher_temperature between 0.5 and 0.7. A value outside that range fails validation.
• openai.gpt-oss-120b-thinking runs at medium reasoning effort by default, so it reasons for longer before answering.

Using these in your config

Set both in the base section, using the exact values from the tables above:

base:
  task: question-answering
  student_model_name: Llama-3.2-1B-Instruct
  teacher_model_name: openai.gpt-oss-120b

Next

Every other parameter and its default: Config file.