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: 4096
Epochs: 1
Learning rate: 2e-06
LoRA: r=64, alpha=128

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: u-10bei/dbbench_sft_dataset_react_v4, u-10bei/dbbench_sft_dataset_react_v3, u-10bei/dbbench_sft_dataset_react_v2, u-10bei/dbbench_sft_dataset_react

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

yuyuchily/agentbench-qwen25-7b-db-max2

AgentBench SFT LoRA Adapter

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

yuyuchily/
agentbench-qwen25-7b-db-max2