102 lines
3.8 KiB
Markdown
102 lines
3.8 KiB
Markdown
|
---
|
|||
|
|
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.*
|