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metadata
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

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
VibeThinker-3B-Hermes-Q3_K_M.gguf Q3_K_M 1.5 GB
VibeThinker-3B-Hermes-Q4_K_M.gguf Q4_K_M (recommended) 1.8 GB
VibeThinker-3B-Hermes-Q5_K_M.gguf Q5_K_M 2.1 GB
VibeThinker-3B-Hermes-Q6_K.gguf Q6_K 2.4 GB
VibeThinker-3B-Hermes-Q8_0.gguf Q8_0 3.1 GB
VibeThinker-3B-Hermes-f16.gguf F16 5.8 GB

Usage (llama.cpp)

llama-cli -m VibeThinker-3B-Hermes-Q4_K_M.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).