astom-M/matsuo-llm-advanced-household-agent
The astom-M/matsuo-llm-advanced-household-agent is a 7.6 billion parameter language model, fine-tuned from Qwen/Qwen2.5-7B-Instruct, specifically designed for household navigation and manipulation tasks. It was trained using Supervised Fine-Tuning (SFT) with LoRA on 50,000 synthetic samples generated by a rule-based Python simulator. This model excels at interpreting and executing household tasks, generating responses in a THOUGHT/ACTION dialogue format.
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Overview
This model, astom-M/matsuo-llm-advanced-household-agent, is a 7.6 billion parameter language model fine-tuned from the Qwen/Qwen2.5-7B-Instruct base model. It is specialized for understanding and performing household navigation and manipulation tasks, making it suitable for robotic control or simulated agent environments.
Key Capabilities
- Household Task Execution: Designed to process task instructions related to household environments, such as "put a laptop on desk."
- THOUGHT/ACTION Generation: Generates structured responses in a
THOUGHT: [reasoning] ACTION: [command]format, facilitating direct integration with agent systems. - Synthetic Data Training: Trained on 50,000 unique synthetic household task trajectories, covering 50 object types, 24 receptacle types, and 6 room types, all generated by a rule-based Python simulator.
- LoRA Fine-Tuning: Utilizes LoRA (r=32, alpha=32) for efficient adaptation of the base model to the specific domain of household tasks.
Good For
- Simulated Household Agents: Ideal for developing and testing agents in virtual household environments that require precise task execution.
- Robotics Research: Can serve as a language interface for robots performing domestic chores or object manipulation.
- Task Planning: Useful for generating step-by-step plans and actions for complex household tasks based on natural language instructions.