Qwen-4B-DB-AlfWorld-v2

Qwen-4B-DB-AlfWorld-v2

This repository provides a merged model fine-tuned from Qwen/Qwen3-4B-Instruct-2507 on datasets u-10bei/sft_alfworld_trajectory_dataset_v5, dbbench_sft_dataset_react_v2 and dbbench_sft_dataset_react_v3.

All LoRA adapter weights have been merged into the base model, and the resulting merged model is saved here as a standalone model.
No external adapter loading is required.

Training Objective

This model is trained to improve multi-turn agent task performance on ALFWorld (household tasks) and DBBench (database operations).

Loss is applied to all assistant turns in the multi-turn trajectory, enabling the model to learn environment observation, action selection, tool use, and recovery from errors.

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: LoRA (merged into final weights)
• Max sequence length: 2048
• Learning rate: 1e-06
• LoRA parameters used during training: r=16, alpha=32

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = Umiharu/Qwen-4B-DB-AlfWorld-v2

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

prompt = "Hello!"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Sources & Terms (IMPORTANT)

Training data: u-10bei/sft_alfworld_trajectory_dataset_v5, dbbench_sft_dataset_react_v2 and dbbench_sft_dataset_react_v3

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.