iromu/Qwen3-0.6B-tools
The iromu/Qwen3-0.6B-tools model is a 0.8 billion parameter Qwen3-based causal language model, fine-tuned with LoRA for enhanced tool calling and agent-style interactions. It features a 32768-token context length and is specifically optimized for structured function calling and multi-step agentic workflows. This model is designed for small-footprint on-device or edge deployments where efficient tool use is critical.
Loading preview...
Model Overview
iromu/Qwen3-0.6B-tools is a 0.8 billion parameter model based on the Qwen3 architecture, specifically fine-tuned using LoRA for robust tool calling and agent-style interactions. It leverages a 32768-token context window, making it suitable for complex multi-step tasks requiring function execution.
Key Capabilities
- Structured Tool/Function Calling: Achieves significantly improved performance in tool call emission and argument matching compared to its base model, with the BF16 version reaching 66.0% exact-args match.
- Agent-Style Interactions: Optimized for multi-step conversational agents that require external tool use.
- Efficient Deployment: Designed for small-footprint applications, including on-device or edge deployments, due to its compact size.
Training Details
The model was fine-tuned from Qwen/Qwen3-0.6B using NVIDIA NeMo AutoModel with LoRA (dimension 32, alpha 32, dropout 0.05) on the sft_tools split of the r0b0tlab/qwen3.8-max-glm5.2-kimi-k3-distillation dataset. Training involved 336 steps with a max sequence length of 4096 and a learning rate of 5e-5, utilizing bf16 mixed precision.
Intended Use Cases
This model is ideal for developers building applications that require:
- Reliable and accurate function calling from natural language prompts.
- Autonomous agents capable of interacting with external systems or APIs.
- Deployment in resource-constrained environments where larger models are impractical.