You can deploy this model and start earning money today!

TSerizawa/
llm-lecture-2025_advanced_make_data_2_model_qwen3-4b-agent-trajectory-lora

Name: llm-lecture-2025_advanced_make_data_2_model_qwen3-4b-agent-trajectory-lora
Availability: InStock

u-10bei/sft_alfworld_trajectory_dataset_v5

Qwen/Qwen3-4B-Instruct-2507

adapter

apache-2.0

Views

No views yet

base_model(qwen3-4b-agent-trajectory-lora)

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 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: 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: u-10bei/sft_alfworld_trajectory_dataset_v5

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.

TSerizawa/llm-lecture-2025_advanced_make_data_2_model_qwen3-4b-agent-trajectory-lora