LakoMoor/QClaw-4B
LakoMoor/QClaw-4B is a 4.5 billion parameter decoder-only transformer model fine-tuned for agentic tasks and tool use. Designed for compatibility with OpenClaw frameworks, it achieves state-of-the-art results in the 4B class on the ClawBench agent benchmark. This model excels at multi-step reasoning and tool calling, making it suitable for compact, efficient agentic pipelines.
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QClaw-4B: A Compact Agentic Model
QClaw-4B, developed by LakoMoor, is a 4.5 billion parameter language model specifically fine-tuned for agentic tasks and tool use. It is designed to integrate seamlessly with OpenClaw-compatible agent frameworks.
Key Capabilities & Performance
Despite its relatively small size, QClaw-4B demonstrates impressive performance, matching or exceeding larger models like Kimi K2.5 and GLM-4.5 on the ClawBench agent benchmark. It achieved an Overall Score of 84.8/100 with a 73.5% pass rate across 1110 tasks, indicating strong capabilities in multi-step planning and tool invocation. The model is considered state-of-the-art in the \u22644B parameter class for agentic workflows.
Training & Architecture
QClaw-4B is built on a decoder-only transformer architecture. Its training involved a curated mixture of:
- Agentic task trajectories (tool calling, multi-step planning)
- Instruction-following data
- Code and structured reasoning
Intended Use Cases
This model is particularly well-suited for:
- Agentic pipelines utilizing OpenClaw or similar frameworks.
- Developing tool-augmented assistants that require efficient and compact inference.
- Research into the capabilities of small models for agentic applications.
It is important to note that QClaw-4B is not intended for safety-critical systems without additional alignment work.