qwen2.5-7b-agent-trajectory-lora-v3_35_1

Name: qwen2_5-7b-agent-lora-sft-v35_1
Availability: InStock

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

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.

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=32, alpha=32

Usage

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

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "o-ckun/qwen2.5-7b-agent-trajectory-lora-v3_35_1"

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;openai/gsm8k

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.

o-ckun/qwen2_5-7b-agent-lora-sft-v35_1

qwen2.5-7b-agent-trajectory-lora-v3_35_1

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

o-ckun/
qwen2_5-7b-agent-lora-sft-v35_1