license, base_model, datasets, language, pipeline_tag, tags

license	base_model	datasets	language	pipeline_tag	tags
apache-2.0	WeiboAI/VibeThinker-3B	lambda/hermes-agent-reasoning-traces	en	text-generation	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
…-Q8_0.gguf	Q8_0	3.1 GB
…-f16.gguf	F16	5.8 GB

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).