102 lines
3.8 KiB
Markdown
102 lines
3.8 KiB
Markdown
---
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license: apache-2.0
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base_model: WeiboAI/VibeThinker-3B
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datasets:
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- lambda/hermes-agent-reasoning-traces
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- tool-calling
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- function-calling
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- multi-turn
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- agentic
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- hermes
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- reasoning
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- qwen2
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---
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# 🛠️🧠 RefinedToolCall-V5-3B
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### A 3B model that *reasons* and *calls tools* — and actually holds a multi-turn conversation.
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**Math-grade reasoning · real function calling · multi-turn agentic · 2.5 GB · runs on your laptop.**
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> `ollama run refinedneuro/refinedtoolcallv5-3b`
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---
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## Why it's different
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Most 3B tool-callers nail a single function call and then fall apart the moment the task spans
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several turns. **RefinedToolCall-V5** was built specifically to fix that — and the numbers moved on
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*every* axis at once, not just the one we were targeting.
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- 🔁 **Multi-turn agentic that actually works** — **~3.7× better** at stateful, multi-step
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tool-use (Berkeley Function-Calling Leaderboard `multi_turn`) than where we started.
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- 🛠️ **Sharper single-turn calling** — **70.7%** on BFCL single-turn (held-out), our best ever.
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- 💪 **Recovers from tool errors** — **0.896** recovery rate; it diagnoses failures instead of
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looping on them.
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- 🧮 **Reasoning fully intact** — **AIME-2024 pass@8 0.933**, unchanged by all the tool training.
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- ⚡ **Tiny & local** — 3B params, **2.5 GB** Q6_K, one command on Ollama, no GPU required.
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- 🆓 **Apache-2.0** — use it, ship it, fine-tune it.
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---
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## The receipts (all held-out, canary-gated)
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| capability | this model |
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|---|---|
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| 🔁 Multi-turn agentic (BFCL `multi_turn`, k=3) | **0.220 avg / 0.298 pass@3** |
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| 🛠️ Single-turn function calling (BFCL, held-out) | **0.707** |
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| 🩹 Recovery from tool errors (n=250) | **0.896** |
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| 🧮 Reasoning (AIME-2024 pass@8) | **0.933** |
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Every number is the **best across five fine-tuning rounds** — multi-turn, single-turn, recovery,
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*and* reasoning all peaked together.
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---
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## How we got here (and why it generalizes)
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We didn't just throw data at it. Five disciplined rounds, each one gated so it could **never**
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regress reasoning or recovery:
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1. **Grounding** — stop inventing shell commands; call the actual functions.
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2. **Plan + finish** — think before calling, and know when the turn is done.
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3. **Scale + long context** — harder tasks, up to 24k tokens.
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4. **On-policy self-improvement (the breakthrough)** — the model learns from its *own* successful
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multi-turn solutions (expert iteration), which broke past the imitation ceiling **and** sharpened
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single-turn calling and error-recovery as a bonus.
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---
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## Quick start
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**Ollama**
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```bash
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ollama run refinedneuro/refinedtoolcallv5-3b # latest = Q6_K, 2.5 GB
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```
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> 💡 Use **Q6_K or higher** for tool-calling — lower quants corrupt the call tokens.
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**Format:** ChatML + Hermes tools. Each turn the model emits a `<think>` plan → one or more
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`<tool_call>` blocks → a final reply. Recommended: temp 0.6, top_p 0.95, repeat_penalty 1.1.
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---
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## Great for
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✅ Local/offline agentic tool-use prototypes ✅ Multi-step function-calling assistants
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✅ Math & STEM reasoning ✅ Learning how small agentic models are actually built.
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## Be honest with me (research preview)
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⚠️ It's a **3B research preview**. Multi-turn is **dramatically improved (~3.7×) but not solved** —
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very long, open-ended autonomous loops can still write buggy code or mis-plan. A brilliant,
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tiny building block; not yet a drop-in autonomous engineer.
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---
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*Built on [WeiboAI/VibeThinker-3B](https://huggingface.co/WeiboAI/VibeThinker-3B) +
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[lambda/hermes-agent-reasoning-traces](https://huggingface.co/datasets/lambda/hermes-agent-reasoning-traces).
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Trained with distribution-matched RFT + on-policy expert iteration, every checkpoint gated against
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reasoning/recovery canaries. Apache-2.0.*
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