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: unsloth/Qwen2.5-7B-Instruct
Method: LoRA (full precision base)
Max sequence length: 4096
Epochs: 2
Learning rate: 2e-05
LoRA: r=64, alpha=128

Usage

python

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

base = "unsloth/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/sft_alfworld_trajectory_dataset_v5 (ALFWorld v5, success-only trajectories)
u-10bei/dbbench_sft_dataset_react_v4 (DBBench v4, all samples)

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.

Rakushaking/Qwen2.5-7b-SFT-Agent-d1

＜【課題】ここは自分で記入して下さい＞

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

Rakushaking/
Qwen2.5-7b-SFT-Agent-d1