This adapter is trained to improve multi-turn agent task performance on both 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, SQL construction, and recovery from errors.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: LoRA (full precision base) + Unsloth
Datasets: ALFWorld v5 (success only) + DBBench v4 (mixed)
Max sequence length: 2048
Epochs: 2
Learning rate: 2e-6
Batch size: 2 x grad_accum 4 = effective 8
LoRA: r=64, alpha=128
Scheduler: cosine with warmup 10%
Loss: all assistant turns (multi-turn tool-use pattern)

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "Chattso-GPT/adv-sft-v2"

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:

u-10bei/sft_alfworld_trajectory_dataset_v5
u-10bei/dbbench_sft_dataset_react_v4

Dataset License: MIT License. Compliance: Users must comply with the MIT license and the base model terms of use.