TToyo2511/ttoyo_advance_2c19

Qwen3-4B Agent SFT for ALFWorld & DBBench TrainingData+ 20260301-2c19

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using LoRA + Unsloth.

This repository contains LoRA adapter weights only. The base model must be loaded separately.

Training Objective

This adapter 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 (full precision base)
• Max sequence length: 2048
• Epochs: 2
• Learning rate: 5e-06
• LoRA: r=128, alpha=128
• Warmup Ratio: 0.08 (reduced from 0.1 in 2c14)

Key Changes in 2c19

Based on 2c14 (best score 4.8386), with minimal change:

1. Sample configuration: 100% identical to 2c14

   • ALF samples: 8 (same)
   • DB samples: 5 (same)
   • Upsampling: ALF 85x, DB 55x (same)
   • DBBench data: 5% (same)

2. Only change: WARMUP_RATIO

   • 0.1 → 0.08 (reduced by 20%)
   • More training steps used for actual learning
   • Minimal impact on data composition

Rationale:

• 2c17/2c18 showed that changing LR, upsampling ratios caused major ALF degradation
• WARMUP_RATIO doesn't affect data composition, so ALF impact should be minimal

Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "TToyo2511/ttoyo_advance_2c19" #★TTT20260301 2c19版

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 (5% sampled)

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.