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Model: sunkencity/qwen25-3b-openclaw
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-3B-Instruct
+tags:
+  - tool-use
+  - function-calling
+  - qwen2.5
+  - mlx
+  - lora
+  - openclaw
+  - localclaw
+  - vllm
+  - agent
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# qwen25-3b-openclaw
+
+A **Qwen2.5-3B-Instruct** model fine-tuned for exceptional tool/function calling ability, purpose-built as the local agent model for [OpenClaw](https://github.com/openclaw/openclaw) / LocalClaw served via vLLM.
+
+## Model Summary
+
+| Property | Value |
+|----------|-------|
+| **Base model** | [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) |
+| **Fine-tuning method** | LoRA (rank=16, alpha=32, all 32 layers) |
+| **Training framework** | [mlx-lm](https://github.com/ml-explore/mlx-lm) on Apple M4 Max |
+| **Training data** | ~57k tool-call examples (hermes-function-calling-v1 + glaive-function-calling-v2) |
+| **Training steps** | 600 |
+| **Tool format** | `<tool_call>` / `</tool_call>` (Qwen/Hermes convention) |
+| **Serving target** | vLLM with `--enable-auto-tool-choice --tool-call-parser hermes` |
+
+## Evaluation
+
+Evaluated on a held-out set of 50 tool-calling examples from the training distribution.
+
+| Metric | Score |
+|--------|-------|
+| **tool_score** (composite) | **0.989** |
+| name_accuracy | 1.000 |
+| arg_f1 | 0.983 |
+| parse_rate | 0.980 |
+| val_loss | 0.010 |
+
+- **name_accuracy**: fraction of examples where the correct function name was called
+- **arg_f1**: token-level F1 between predicted and ground-truth arguments
+- **parse_rate**: fraction of outputs that contained a valid, parseable `<tool_call>` block
+- **tool_score**: `name_accuracy × 0.4 + arg_f1 × 0.4 + parse_rate × 0.2`
+
+## What It's Good For
+
+- **OpenClaw / LocalClaw agent** — drop-in local model for the tool-calling tier; handles calendars, email, web search, browser control, and custom skill tools
+- **Any OpenAI-compatible tool-use pipeline** — responds to the standard `tools` parameter and produces structured function calls
+- **Offline / privacy-first deployments** — 3B parameters runs fast on Apple Silicon or a modest GPU; no cloud dependency
+- **Multi-tool selection** — trained on examples with multiple available tools; reliably selects the right one
+- **Argument extraction** — near-perfect extraction of typed arguments from natural-language queries
+
+## What It's Not Good For
+
+- Long multi-turn reasoning chains (consider a larger model for orchestration)
+- Tasks requiring no tools — the model is biased toward calling tools when they're available
+- Languages other than English (training data is English-only)
+
+## Tool Call Format
+
+The model outputs tool calls using the Hermes `<tool_call>` convention:
+
+```
+<tool_call>
+{"name": "function_name", "arguments": {"arg1": "value1", "arg2": 42}}
+</tool_call>
+```
+
+Multiple parallel calls are output sequentially, each in its own block.
+
+The system prompt should include available tools in `<tools></tools>` XML tags:
+
+```
+You are a function calling AI model. You are provided with function signatures
+within <tools> </tools> XML tags. You may call one or more functions to assist
+with the user query.
+
+<tools>
+{"type": "function", "function": {"name": "get_weather", "description": "...", "parameters": {...}}}
+</tools>
+
+For each function call return a json object within <tool_call> </tool_call> tags.
+```
+
+## Quick Start with vLLM
+
+```bash
+pip install vllm
+
+vllm serve sunkencity/qwen25-3b-openclaw \
+  --enable-auto-tool-choice \
+  --tool-call-parser hermes \
+  --port 8000
+```
+
+This exposes an OpenAI-compatible API at `http://localhost:8000/v1` with structured `tool_calls` in responses.
+
+## Integration with OpenClaw / LocalClaw
+
+LocalClaw auto-discovers models from a running vLLM server. After starting the server above, add to `~/.localclaw/openclaw.local.json`:
+
+```json
+{
+  "agents": {
+    "defaults": {
+      "model": {
+        "primary": "vllm/sunkencity/qwen25-3b-openclaw"
+      }
+    }
+  }
+}
+```
+
+LocalClaw will route tool-calling tasks to this model automatically via the three-tier routing system.
+
+## Python Usage (mlx-lm)
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load("sunkencity/qwen25-3b-openclaw")
+
+messages = [
+    {
+        "role": "system",
+        "content": (
+            "You are a function calling AI model.\n\n"
+            "<tools>\n"
+            '{"type": "function", "function": {"name": "get_weather", '
+            '"description": "Get current weather", "parameters": {"type": "object", '
+            '"properties": {"location": {"type": "string"}}, "required": ["location"]}}}\n'
+            "</tools>\n\n"
+            "For each function call return a json object within <tool_call> </tool_call> tags."
+        ),
+    },
+    {"role": "user", "content": "What is the weather in San Francisco?"},
+]
+
+prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+response = generate(model, tokenizer, prompt=prompt, max_tokens=256, verbose=False)
+print(response)
+# <tool_call>
+# {"name": "get_weather", "arguments": {"location": "San Francisco"}}
+# </tool_call>
+```
+
+## Training Details
+
+**Datasets:**
+- [NousResearch/hermes-function-calling-v1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1) — 1,883 examples, already in `<tool_call>` format
+- [glaiveai/glaive-function-calling-v2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2) — 55,000 examples converted from `<functioncall>` format
+
+**Preprocessing:**
+- Conversations truncated to the first tool-call turn (system + user + assistant) to ensure tool call output is always within the sequence budget
+- `<functioncall>` → `<tool_call>` normalization including balanced-brace extraction and single-quoted argument repair
+- 90/5/5 train/valid/eval split
+
+**LoRA config:**
+```yaml
+fine_tune_type: lora
+num_layers: 32          # all transformer layers
+lora_parameters:
+  rank: 16
+  alpha: 32
+  dropout: 0.0
+  scale: 10.0
+optimizer: adamw
+learning_rate: 2e-4
+batch_size: 4
+iters: 600
+max_seq_length: 2048
+mask_prompt: true       # loss only on assistant turns
+grad_checkpoint: true
+```
+
+**Hardware:** Apple M4 Max (128 GB unified memory), ~34 minutes wall-clock
+
+## Training Code
+
+Training was performed using the [tool-tuner](https://github.com/sunkencity/tool-tuner) framework — an autoresearch-inspired autonomous LoRA experiment loop built with mlx-lm.
+
+## License
+
+Apache 2.0 — same as base model.

added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. 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 Qwen, created by Alibaba Cloud. You 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 %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": 151643,
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 11008,
+    "max_position_embeddings": 32768,
+    "max_window_layers": 70,
+    "model_type": "qwen2",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 36,
+    "num_key_value_heads": 2,
+    "rms_norm_eps": 1e-06,
+    "rope_theta": 1000000.0,
+    "sliding_window": 32768,
+    "tie_word_embeddings": true,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.43.1",
+    "use_cache": true,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

generation_config.json

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+  "bos_token_id": 151643,
+  "pad_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_p": 0.8,
+  "top_k": 20,
+  "transformers_version": "4.37.0"
+}

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