Olmo 3 7B Think vs SmolLM3-3B

Olmo 3 7B Think

A fully open 7B reasoning cousin for maths, code and step-by-step answers, with every dataset and checkpoint published and its safety results rerunnable. Ollama's 4-bit build needs about 5.4 GB. It always thinks, wasn't trained for tool calls, has 65K of context, and Ai2 hasn't answered its discussions.

SmolLM3-3B

A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 2025.

Olmo 3 7B Think

1. Quickest start is ollama run olmo-3:7b-think, about 4.5 GB
2. With vLLM, vllm serve allenai/Olmo-3-7B-Think --max-model-len 65536; vLLM runs it through its Transformers backend
3. Sample at temperature 0.6 and top_p 0.95 with up to 32,768 output tokens, as the card recommends, since thinking traces are long
4. Every reply starts with a think block; strip it yourself or use a reasoning parser that reads think tags
5. Pass your own system prompt; the template's default says the cut-off is November 2024 and that no functions are available
6. For tool calls use Olmo 3 7B Instruct instead, which Ai2 trained for function calling
7. A discussion reports endless reasoning loops when served with vLLM, unanswered, so cap max_tokens
8. Check the use against Ai2's Responsible Use Guidelines, which bar automated social-media posting and undisclosed automated content outside research

SmolLM3-3B

1. Start it with vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text
2. Pass chat_template_kwargs with enable_thinking false for short tasks, since thinking is on by default; a /think or /no_think flag in the system prompt overrides the kwarg
3. Put earlier tool calls in the assistant message content, since the template ignores a tool_calls field when it renders history
4. Add the YaRN rope_scaling block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens
5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling
6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
7. Validate JSON output yourself or use the runtime's guided decoding