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.

Dataset Processing (Custom Filtering)

To improve the reasoning efficiency and reduce the risk of infinite loops (repetitive actions), the training dataset was customized with the following filtering strategy:

Robustness Maintenance: Trajectories with 0, 2, 3 detours were retained.

Training Configuration

Base model: Qwen/Qwen2.5-7B-Instruct
Method: LoRA (full precision base)
Max sequence length: 2048
Epochs: 2
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: alfworld_v5_filtered_023.jsonl

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.

naru0411/LLM-Competition-advanced-003

qwen2.5-7b-agent-trajectory-lora

Training Objective

Dataset Processing (Custom Filtering)

Training Configuration

Usage

Sources & Terms (IMPORTANT)

naru0411/
LLM-Competition-advanced-003