🛠️🧠 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
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.
The receipts (all held-out, canary-gated)
| capability | this model |
|---|---|
🔁 Multi-turn agentic (BFCL multi_turn, k=3) |
0.220 avg / 0.298 pass@3 |
| 🛠️ Single-turn function calling (BFCL, held-out) | 0.707 |
| 🩹 Recovery from tool errors (n=250) | 0.896 |
| 🧮 Reasoning (AIME-2024 pass@8) | 0.933 |
Every number is the best across five fine-tuning rounds — multi-turn, single-turn, recovery, and reasoning all peaked together.
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:
- Grounding — stop inventing shell commands; call the actual functions.
- Plan + finish — think before calling, and know when the turn is done.
- Scale + long context — harder tasks, up to 24k tokens.
- 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.
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.
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.
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.