---
license: apache-2.0
base_model: WeiboAI/VibeThinker-3B
datasets:
  - lambda/hermes-agent-reasoning-traces
language:
  - en
pipeline_tag: text-generation
tags:
  - tool-calling
  - function-calling
  - multi-turn
  - agentic
  - hermes
  - reasoning
  - qwen2
---

# 🛠️🧠 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:

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.

---

## Quick start

**Ollama**
```bash
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](https://huggingface.co/WeiboAI/VibeThinker-3B) +
[lambda/hermes-agent-reasoning-traces](https://huggingface.co/datasets/lambda/hermes-agent-reasoning-traces).
Trained with distribution-matched RFT + on-policy expert iteration, every checkpoint gated against
reasoning/recovery canaries. Apache-2.0.*