--- base_model: Qwen/Qwen3-4B-Instruct-2507 datasets: - u-10bei/dbbench_sft_dataset_react - u-10bei/dbbench_sft_dataset_react_v2 - u-10bei/dbbench_sft_dataset_react_v3 - u-10bei/dbbench_sft_dataset_react_v4 language: - en license: apache-2.0 library_name: transformers pipeline_tag: text-generation tags: - unsloth - agent - tool-use - dbbench --- # Qwen3-4B-Agent-DBBench-Specialist This repository provides a **merged full-parameter model** (bfloat16) fine-tuned from **Qwen/Qwen3-4B-Instruct-2507**. Instead of a standalone LoRA adapter, this model has been created by merging LoRA weights back into the base model using **Unsloth's `merge_and_unload`** method. This ensures high-speed inference and easy deployment. ## Training Objective This model is specialized for **DBBench trajectory tasks**, trained to handle multi-turn environment observations and action selections. ## Training Configuration - **Base model**: Qwen/Qwen3-4B-Instruct-2507 - **Format**: Merged Full Weights (bfloat16) - **Method**: LoRA fine-tuning (Merged via Unsloth `merge_and_unload`) - **Max sequence length**: 4096 - **Steps**: 500 - **Learning rate**: 5e-07 - **LoRA Parameters during training**: r=64, alpha=128 - **Platform**: Trained with Unsloth ## Usage Since this is a merged model, you can load it directly like any other Qwen3 model: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch model_id = "moushi21/agent-bench-dbbench-merged4" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto" ) ``` ## Sources & Terms (IMPORTANT) Training data: - u-10bei/dbbench_sft_dataset_react - u-10bei/dbbench_sft_dataset_react_v2 - u-10bei/dbbench_sft_dataset_react_v3 - u-10bei/dbbench_sft_dataset_react_v4 Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License. Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.