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Model: RefinedNeuro/RefinedToolCallV5-3b Source: Original Platform
2026-07-24 02:41:09 +08:00
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--- a/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
+  - 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.*
--- a/RefinedToolCallV5-3b-Q6_K.gguf
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--- a/chat_template.jinja
+++ b/chat_template.jinja
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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}
--- a/config.json
+++ b/config.json
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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
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+    "full_attention",
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+  "max_position_embeddings": 131072,
+  "max_window_layers": 36,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
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+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.7.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
--- a/generation_config.json
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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
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+    151643
+  ],
+  "max_new_tokens": 65536,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "5.7.0"
+}
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+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "is_local": true,
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+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}