sunkencity/qwen25-3b-openclaw

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kPublished:Mar 19, 2026License:apache-2.0Architecture:Transformer Open Weights Warm

sunkencity/qwen25-3b-openclaw is a 3.1 billion parameter Qwen2.5-3B-Instruct model fine-tuned by sunkencity for exceptional tool/function calling abilities. Optimized for use as a local agent model within the OpenClaw/LocalClaw framework, it excels at structured function calls and argument extraction. This model is designed for offline, privacy-first deployments, running efficiently on Apple Silicon or modest GPUs with a 32768 token context length.

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Model Overview

sunkencity/qwen25-3b-openclaw is a 3.1 billion parameter model based on Qwen/Qwen2.5-3B-Instruct, specifically fine-tuned for robust tool and function calling. Developed by sunkencity, this model leverages LoRA fine-tuning (rank=16, alpha=32 across all 32 layers) on approximately 57,000 tool-call examples, combining data from hermes-function-calling-v1 and glaive-function-calling-v2.

Key Capabilities

  • Exceptional Tool Calling: Achieves a tool_score of 0.989 on a held-out evaluation set, demonstrating high accuracy in function name identification (1.000 name_accuracy) and argument extraction (0.983 arg_f1).
  • Structured Output: Produces tool calls in the Hermes <tool_call> JSON format, compatible with OpenAI-style tool-use pipelines.
  • Multi-tool Selection: Reliably selects the correct tool even when multiple options are available.
  • Efficient Deployment: With 3.1 billion parameters, it's suitable for offline and privacy-first deployments, running fast on Apple Silicon or modest GPUs.
  • OpenClaw Integration: Purpose-built as a local agent model for OpenClaw / LocalClaw, handling tasks like calendars, email, web search, and custom skills.

Ideal Use Cases

  • OpenClaw / LocalClaw Agent: Serves as a drop-in local model for the tool-calling tier within the OpenClaw ecosystem.
  • OpenAI-Compatible Tool-Use Pipelines: Responds to the standard tools parameter and generates structured function calls.
  • Offline & Privacy-First Applications: Enables local execution of tool-calling tasks without cloud dependencies.
  • Argument Extraction: Highly effective at extracting typed arguments from natural language queries.

Limitations

  • Not recommended for long multi-turn reasoning chains; larger models are better suited for orchestration.
  • Biased towards calling tools when available, making it less ideal for tasks requiring no tools.
  • Training data is English-only, limiting its performance in other languages.

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