thetmon/c19
The thetmon/c19 is a 4 billion parameter LoRA adapter, fine-tuned from Qwen/Qwen3-4B-Instruct-2507, designed to enhance multi-turn agent task performance. This adapter specializes in improving capabilities for household tasks (ALFWorld) and database operations (DBBench) by learning environment observation, action selection, and tool use. It focuses on improving agentic reasoning and error recovery within complex, multi-turn interactions.
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thetmon/c19: Qwen3-4B Agentic LoRA Adapter
This repository provides a LoRA adapter for the Qwen3-4B-Instruct-2507 base model, specifically engineered to boost performance in complex, multi-turn agent tasks. Unlike general-purpose instruction models, this adapter is fine-tuned to excel in scenarios requiring sequential decision-making and interaction with environments.
Key Capabilities
- Enhanced Multi-Turn Agent Performance: Optimized for tasks that involve a series of actions and observations, such as those found in ALFWorld and DBBench.
- Agentic Reasoning: Improves the model's ability to understand environment states, select appropriate actions, and utilize tools effectively.
- Error Recovery: Training includes loss applied to all assistant turns, enabling the model to learn from and recover from errors within a trajectory.
- Specialized for ALFWorld & DBBench: Directly trained on datasets for household task automation and database operations, making it highly effective for these domains.
Training Details
- Base Model: Qwen/Qwen3-4B-Instruct-2507
- Methodology: LoRA (r=64, alpha=128) with full precision base, trained for 3 epochs.
- Max Sequence Length: 4096 tokens.
- Training Data: Utilizes u-10bei/sft_alfworld_trajectory_dataset_v5 and u-10bei/dbbench_sft_dataset_react_v4, both under MIT License.
When to Use This Model
This adapter is ideal for developers building AI agents that need to perform multi-step tasks, interact with environments, and demonstrate robust decision-making. It's particularly well-suited for applications in automated task execution, intelligent assistants requiring tool use, and scenarios demanding agentic capabilities over simple question-answering.