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Model: RefinedNeuro/VibeThinker-3B-Hermes-GGUF
Source: Original Platform

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+VibeThinker-3B-Hermes-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+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](https://huggingface.co/RefinedNeuro/RefinedToolCallV5-3b) — 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`](https://huggingface.co/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)
+
+```bash
+# 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).

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