o-ckun/qwen2_5-7b-agent-lora-sft-v4

qwen2.5-7b-agent-trajectory-lora-v4

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

This repository contains the full-merged 16-bit weights. No adapter loading is required.

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: unsloth/Qwen2.5-7B-Instruct
• Method: LoRA (full precision base)
• Max sequence length: 2048
• Epochs: 3
• Learning rate: 3e-05
• LoRA: r=64, alpha=64

Usage

Since this is a merged model, you can use it directly with transformers.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your_id/your-repo-name"

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

Sources & Terms (IMPORTANT)

Training data: u-10bei/dbbench_sft_dataset_react;u-10bei/dbbench_sft_dataset_react_v2;u-10bei/dbbench_sft_dataset_react_v3;u-10bei/dbbench_sft_dataset_react_v4;u-10bei/sft_alfworld_trajectory_dataset_v2

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.