qwen25_7b_agentbench_lora_trained

qwen25_7b_agentbench_lora_trained

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen2.5-7B-Instruct using LoRA + Unsloth + Single-Phase Training.

Note: 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.

Single-Phase Training Strategy

Training Configuration

Training Results

Usage

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

base = "Qwen/Qwen2.5-7B-Instruct"
adapter = "tomoniyukiwo/qwen25_7b_agentbench_lora_trained"

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

With Unsloth (faster)

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="tomoniyukiwo/qwen25_7b_agentbench_lora_trained",
    max_seq_length=4096,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

Sources & Terms

• Training data: u-10bei/dbbench_sft_dataset_react_v4
• Dataset License: MIT License
• Base model: Qwen/Qwen2.5-7B-Instruct
• Compliance: Users must comply with the MIT license and the base model's original terms of use.