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