This adapter is trained to improve multi-turn agent task performance on ALFWorld (household tasks) and DBBench (database operations) using mixed training (ALFWorld + DBBench combined dataset).

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/Qwen2.5-7B-Instruct
Method: LoRA (full precision base)
Max sequence length: 2048
Epochs: 2
Learning rate: 2e-06
LoRA: r=32, alpha=64

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen2.5-7B-Instruct"
adapter = "your_id/your-repo"

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

Sources & Terms (IMPORTANT)

Training data:

ALFWorld: u-10bei/sft_alfworld_trajectory_dataset_v5
DBBench: u-10bei/dbbench_sft_dataset_react_v4

Dataset License: MIT License. Both datasets are 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.

tmdoi/FAE_260301_Qwen2_v_3_2_mix

Third model 2026-03-01-m2 / Mixed dataset / qwen2.5-7b

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

tmdoi/
FAE_260301_Qwen2_v_3_2_mix