---

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

- hermes

- reasoning

- gguf

- llama-cpp

- research-preview

---

> 👉 Newer model: RefinedToolCall-V5-3B — multi-turn agentic ~3.7×, single-turn 0.707, recovery 0.896, reasoning intact. ollama run refinedneuro/refinedtoolcallv5-3b

VibeThinker-3B-Hermes — GGUF (Research Preview)

⚠️ Research preview / experimental. Read Limitations before use. Known issues:

repetition loops on out-of-distribution multi-turn input, and over-eagerness to call tools.

GGUF quantizations of a LoRA fine-tune of

WeiboAI/VibeThinker-3B that adds

Hermes-style function calling (<think>…</think> + <tool_call>…</tool_call>) while

preserving reasoning. See the full (transformers) model card for training details and

benchmarks.

Files

| File | Quant | Size |

|---|---|---|

| …-Q6_K.gguf | Q6_K | 2.4 GB | min recommended |

| …-Q8_0.gguf | Q8_0 | 3.1 GB |

| …-f16.gguf | F16 | 5.8 GB | best |

> Why only Q6_K and up? We measured tool-call fidelity on a multi-step agentic task:

> Q6_K, Q8_0 and F16 pass; Q3/Q4/Q5 fail (they emit malformed/incomplete tool calls and

> can loop). Since this is a tool-calling model, the lower quants were removed to avoid

> shipping a broken experience. Use Q6_K for the best size/quality balance.

Usage (llama.cpp)

# use Q6_K+ for tool-calling
llama-cli -m VibeThinker-3B-Hermes-Q6_K.gguf \
  --temp 0.6 --top-p 0.95 --repeat-penalty 1.1 \
  -p "<your Hermes-formatted prompt>"

Recommended settings

• Use a stop on </tool_call> for single-call settings — essential to avoid the model

appending duplicate/hallucinated calls.

• temperature 0.6, top_p 0.95, repeat-penalty ≈ 1.1.
• Stop tokens: <|im_end|> (151645) and <|endoftext|> (151643).
• Hermes system prompt with a <tools> block; the model emits <think>…</think> then

<tool_call>\n{"name": …, "arguments": …}\n</tool_call>.

Benchmarks (summary)

• Reasoning (AIME 2024): base avg@4 0.842 → this model 0.783; pass@4 0.867 unchanged.
• Tool-calling (BFCL single-turn, with stop fix): live_simple ~60 %, live_multiple ~31 %,

live_relevance 87.5 %, live_irrelevance ~34 % (over-eager).

Limitations

• Repetition loops on OOD multi-turn input (emits the same call many times; avg ~25 calls/turn

vs ~1.3 expected). Mitigate with stop=["</tool_call>"] + repeat-penalty.

• Over-eager to call tools (does not always decline when it should).
• Single-turn exact-match is poor without the stop fix.
• ~6-pt avg@4 reasoning dip vs base; trained 1 epoch on one dataset config.

Not recommended for production agents as-is.

License

Apache-2.0. Built on WeiboAI/VibeThinker-3B and lambda/hermes-agent-reasoning-traces

(both Apache-2.0).