Model Selection

Help the user choose the right model for their task.

Reference

• docs/model-lineup.mdx — Full model listing with types, sizes, and architecture
• tinker_cookbook/model_info.py — Model metadata and renderer mapping

Available models

Qwen family

Model	Type	Arch	Size
Qwen/Qwen3.5-397B-A17B	Hybrid + Vision	MoE	Large
Qwen/Qwen3.5-35B-A3B	Hybrid + Vision	MoE	Medium
Qwen/Qwen3.5-27B	Hybrid + Vision	Dense	Medium
Qwen/Qwen3.5-4B	Hybrid + Vision	Dense	Compact
Qwen/Qwen3-235B-A22B-Instruct-2507	Instruction	MoE	Large
Qwen/Qwen3-30B-A3B-Instruct-2507	Instruction	MoE	Medium
Qwen/Qwen3-30B-A3B	Hybrid	MoE	Medium
Qwen/Qwen3-30B-A3B-Base	Base	MoE	Medium
Qwen/Qwen3-32B	Hybrid	Dense	Medium
Qwen/Qwen3-8B	Hybrid	Dense	Small
Qwen/Qwen3-8B-Base	Base	Dense	Small
Qwen/Qwen3-4B-Instruct-2507	Instruction	Dense	Compact
Qwen/Qwen3-VL-235B-A22B-Instruct	Vision	MoE	Large
Qwen/Qwen3-VL-30B-A3B-Instruct	Vision	MoE	Medium

Llama family

Model	Type	Arch	Size
meta-llama/Llama-3.3-70B-Instruct	Instruction	Dense	Large
meta-llama/Llama-3.1-70B	Base	Dense	Large
meta-llama/Llama-3.1-8B	Base	Dense	Small
meta-llama/Llama-3.1-8B-Instruct	Instruction	Dense	Small
meta-llama/Llama-3.2-3B	Base	Dense	Compact
meta-llama/Llama-3.2-1B	Base	Dense	Compact

Nemotron family

Model	Type	Arch	Size
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16	Hybrid	MoE	Large
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16	Hybrid	MoE	Medium

Other families

Model	Type	Arch	Size
openai/gpt-oss-120b	Reasoning	MoE	Medium
openai/gpt-oss-20b	Reasoning	MoE	Small
deepseek-ai/DeepSeek-V3.1	Hybrid	MoE	Large
deepseek-ai/DeepSeek-V3.1-Base	Base	MoE	Large
moonshotai/Kimi-K2-Thinking	Reasoning	MoE	Large
moonshotai/Kimi-K2.5	Reasoning + Vision	MoE	Large

How to choose

By task type

• Instruction tuning / chat SFT: Start with an Instruction model (e.g., Llama-3.1-8B-Instruct, Qwen3-30B-A3B-Instruct-2507)
• RL with verifiable rewards (GRPO): Use Instruction or Hybrid models — they already follow instructions
• Reasoning / chain-of-thought: Use Reasoning or Hybrid models (Kimi-K2-Thinking, Qwen3-8B)
• Full post-training pipeline: Start with a Base model (e.g., Qwen3-8B-Base, Llama-3.1-8B)
• Vision tasks: Use Vision or Hybrid+Vision models (Qwen3.5-35B-A3B, Qwen3-VL-*)
• Distillation (student): Use a Base model as student
• Quick prototyping: Use compact models (Llama-3.2-1B, Qwen3.5-4B)

By cost

Prefer MoE models — they're much more cost-effective than dense models because cost scales with active parameters, not total parameters. For example, Qwen3-30B-A3B (MoE, 3B active) is cheaper than Qwen3-32B (Dense, 32B active) despite similar quality.

Model types explained

• Base: Pre-trained on raw text. For research or full post-training pipelines.
• Instruction: Fine-tuned for instruction following. Fast inference, no chain-of-thought.
• Reasoning: Always uses chain-of-thought before visible output.
• Hybrid: Can operate in both thinking and non-thinking modes.
• Vision: Processes images alongside text. See /renderers skill for vision input handling.

Size categories

• Compact: 1B–4B parameters
• Small: 8B parameters
• Medium: 27B–32B parameters
• Large: 70B+ parameters

Renderer matching

Every model needs a matching renderer. Always use the automatic lookup:

from tinker_cookbook import model_info

renderer_name = model_info.get_recommended_renderer_name(model_name)

Never hardcode renderer names — the mapping is maintained in model_info.py.

Learning rate by model

Use hyperparam_utils.get_lr(model_name) for model-specific LR recommendations. See the /hyperparams skill for details.

Common pitfalls

• MoE models are cheaper than dense — prefer them unless you have a specific reason
• Base models need full post-training (SFT + alignment) to be useful for chat
• Instruction models are best for tasks where you want to start from a capable baseline
• Vision models require ImageChunk in messages — see /renderers skill
• Llama models require HF_TOKEN for tokenizer download (gated on HuggingFace)