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/Qwen3-4B-Instruct-2507
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/Qwen3-4B-Instruct-2507"
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 (mixed):

DB: u-10bei/dbbench_sft_dataset_react_v4
ALF: u-10bei/sft_alfworld_trajectory_dataset_v5 (filtered, max_tokens=900, ratio=0.20)
- keep_ratio=1.00 (applied after length-filter, before ratio selection)

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.

eridon-pro/qwen3-4b-agent-trajectory-lora-25

SFTed Qwen3-4B for Agentbench

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

eridon-pro/
qwen3-4b-agent-trajectory-lora-25