You can deploy this model and start earning money today!

＜qwen3-4b-agent-trajectory-lora＞

Name: advanced-comp-model-20260222181923
Availability: InStock

This repository provides a merged model that includes both the base model unsloth/Qwen3-4B-Instruct-2507 and the LoRA adapter. No separate LoRA 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/Qwen3-4B-Instruct-2507
Method: LoRA
- dtype: torch.bfloat16
- load_in_4bit: False
Max sequence length: 1024
Epochs: 1.2
Learning rate: 1e-06
LoRA: r=64, alpha=128

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "da1ch812/advanced-comp-model-20260222181923"

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/sft_alfworld_trajectory_dataset_v2
u-10bei/sft_alfworld_trajectory_dataset_v3
u-10bei/sft_alfworld_trajectory_dataset_v4
u-10bei/sft_alfworld_trajectory_dataset_v5
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

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.