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/Qwen3-4B-Instruct-2507
Method: LoRA (full precision base)
Max sequence length: 2048
Epochs: 2
Learning rate: 2e-04
LoRA: r=64, alpha=128

Usage

python

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

base = "unsloth/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: kasumi-nakano/alfworld_cosmetics_sft_v1_en_lab

Training data:

kasumi-nakano/alfworld_cosmetics_sft_v1_en_lab
u-10bei/dbbench_sft_dataset_react_v4

Dataset License: Apache 2.0

kasumi-nakano/qwen3-4b-cosmetics-agent

qwen3-4b-cosmetics-agent

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

kasumi-nakano/
qwen3-4b-cosmetics-agent