norecyc/lastbox-gemma4-e2b-v6-toolprior
The norecyc/lastbox-gemma4-e2b-v6-toolprior is a 5.1 billion parameter Gemma-4 based language model developed by norecyc, specifically fine-tuned for enhanced tool emission and agentic behavior. This model achieves a 72% tool emission rate and a 0.608 agentic score, significantly improving over its base model. It is optimized for deployment on resource-constrained devices like the Raspberry Pi 5, making it suitable for edge AI applications requiring robust tool use.
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LastBox v6: Enhanced Tool Emission for Edge Devices
The norecyc/lastbox-gemma4-e2b-v6-toolprior is a 5.1 billion parameter Gemma-4 based model, specifically engineered to dramatically improve tool emission and agentic performance. Developed by norecyc, this version represents a significant leap from its predecessor, lastbox-gemma4-e2b-sft-v3, achieved through a targeted 12-minute SFT (Supervised Fine-Tuning) pass on tool-only pairs.
Key Capabilities & Improvements
- High Tool Emission Rate: Achieves a 72% tool emission rate, a substantial increase from near 0% in the base model.
- Agentic Score: Demonstrates a 38x improvement in agentic score, reaching 0.608.
- Robust Tool Accuracy: Features 64% tool accuracy and 56% argument validity.
- Optimized for Edge Deployment: Designed for efficient operation on devices like the Raspberry Pi 5, utilizing Q4_K_M quantization.
- Targeted SFT Approach: The model's success stems from a focused SFT warmup on 1,034
[user, assistant_tool_call]pairs, setting a strong prior for first-turn tool emission. - System Prompt Dependency: To achieve the stated tool emission rates, the model requires the full training-time system prompt, including a 7-tool definitions JSON block, at inference.
When to Use This Model
- Agentic Applications: Ideal for scenarios requiring reliable tool use and agentic behavior in language models.
- Resource-Constrained Environments: Excellent choice for deployment on edge devices such as the Raspberry Pi 5 due to its optimized quantization.
- Tool-Calling Tasks: Particularly suited for tasks where the model needs to accurately identify and call external tools based on user prompts.