Amouri28/Qwen3-4B-lora-DBBench_repo
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kPublished:Feb 14, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Amouri28/Qwen3-4B-lora-DBBench_repo is a 4 billion parameter LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507. This adapter is specifically designed to enhance multi-turn agent task performance, particularly in household tasks (ALFWorld) and database operations (DBBench). It improves the base model's ability to handle environment observation, action selection, tool use, and error recovery within complex multi-turn trajectories.
Loading preview...
Overview
This repository provides a LoRA adapter for the Qwen/Qwen3-4B-Instruct-2507 base model, developed by Amouri28. The adapter, weighing 4 billion parameters, is fine-tuned using LoRA + Unsloth to significantly improve performance on multi-turn agent tasks.
Key Capabilities
- Enhanced Multi-Turn Agent Performance: Specifically trained to excel in complex, multi-turn interactions.
- Task Specialization: Optimized for household tasks (ALFWorld) and database operations (DBBench).
- Comprehensive Learning: The training objective applies loss to all assistant turns, fostering better environment observation, action selection, tool use, and error recovery.
- Efficient Fine-tuning: Utilizes LoRA (full precision base) with a max sequence length of 2048 and a learning rate of 2e-06.
Good For
- Developers working on agent-based systems requiring robust multi-turn interaction capabilities.
- Applications involving automated household tasks or complex database operations.
- Extending the Qwen3-4B-Instruct-2507 model's functionality for specialized agentic workflows. Users must comply with the MIT License of the training data and the base model's original terms.