RefinedToolCallV5-3b

🛠️🧠 RefinedToolCall-V5-3B

A 3B model that reasons and calls tools — and actually holds a multi-turn conversation.

Math-grade reasoning · real function calling · multi-turn agentic · 2.5 GB · runs on your laptop.

ollama run refinedneuro/refinedtoolcallv5-3b

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Why it's different

Most 3B tool-callers nail a single function call and then fall apart the moment the task spans several turns. RefinedToolCall-V5 was built specifically to fix that — and the numbers moved on every axis at once, not just the one we were targeting.

• 🔁 Multi-turn agentic that actually works — ~3.7× better at stateful, multi-step tool-use (Berkeley Function-Calling Leaderboard multi_turn) than where we started.
• 🛠️ Sharper single-turn calling — 70.7% on BFCL single-turn (held-out), our best ever.
• 💪 Recovers from tool errors — 0.896 recovery rate; it diagnoses failures instead of looping on them.
• 🧮 Reasoning fully intact — AIME-2024 pass@8 0.933, unchanged by all the tool training.
• ⚡ Tiny & local — 3B params, 2.5 GB Q6_K, one command on Ollama, no GPU required.
• 🆓 Apache-2.0 — use it, ship it, fine-tune it.

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The receipts (all held-out, canary-gated)

Every number is the best across five fine-tuning rounds — multi-turn, single-turn, recovery, and reasoning all peaked together.

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How we got here (and why it generalizes)

We didn't just throw data at it. Five disciplined rounds, each one gated so it could never regress reasoning or recovery:

1. Grounding — stop inventing shell commands; call the actual functions.
2. Plan + finish — think before calling, and know when the turn is done.
3. Scale + long context — harder tasks, up to 24k tokens.
4. On-policy self-improvement (the breakthrough) — the model learns from its own successful multi-turn solutions (expert iteration), which broke past the imitation ceiling and sharpened single-turn calling and error-recovery as a bonus.

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Quick start

Ollama

ollama run refinedneuro/refinedtoolcallv5-3b      # latest = Q6_K, 2.5 GB

💡 Use Q6_K or higher for tool-calling — lower quants corrupt the call tokens.

Format: ChatML + Hermes tools. Each turn the model emits a <think> plan → one or more <tool_call> blocks → a final reply. Recommended: temp 0.6, top_p 0.95, repeat_penalty 1.1.

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Great for

✅ Local/offline agentic tool-use prototypes ✅ Multi-step function-calling assistants ✅ Math & STEM reasoning ✅ Learning how small agentic models are actually built.

Be honest with me (research preview)

⚠️ It's a 3B research preview. Multi-turn is dramatically improved (~3.7×) but not solved — very long, open-ended autonomous loops can still write buggy code or mis-plan. A brilliant, tiny building block; not yet a drop-in autonomous engineer.

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Built on WeiboAI/VibeThinker-3B + lambda/hermes-agent-reasoning-traces. Trained with distribution-matched RFT + on-policy expert iteration, every checkpoint gated against reasoning/recovery canaries. Apache-2.0.