qwen3-4b-instruct-lora-sft-advanced-comp-v21

# Qwen3-4B-Instruct LoRA SFT for LLM Advanced Competition (21)

This repository provides a LoRA adapter fine-tuned from OgawaHiroyuki/qwen3-4b-instruct-lora-sft-advanced-comp-v09 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: OgawaHiroyuki/qwen3-4b-instruct-lora-sft-advanced-comp-v09
• Method: LoRA (full precision base)
• Max sequence length: 2048
• Epochs: 2
• Learning rate: 1e-05
• LoRA: r=64, alpha=128

Usage

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

base = "OgawaHiroyuki/qwen3-4b-instruct-lora-sft-advanced-comp-v09"
adapter = "OgawaHiroyuki/qwen3-4b-instruct-lora-sft-advanced-comp-v21"

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/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.