models

name	models
description	Guide for choosing models in Tinker — available model families, model types (base, instruction, reasoning, hybrid, vision), architecture (dense vs MoE), and how to match renderers to models. Use when the user asks which model to use, what models are available, or how to pick a model for their task.

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)