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maskver – AI Model by takayosh | AlphaNeural AI

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takayosh/
maskver

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text-generation

u-10bei/sft_alfworld_trajectory_dataset_v5

Qwen/Qwen3-4B-Instruct-2507

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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 on preprocessed multi-turn agent trajectories for ALFWorld and DBBench.

Data preprocessing includes:

Reformatted trajectory structure
Removal of unnecessary reasoning tokens
Masked training strategy applied to assistant outputs

Loss is applied to selected assistant turns according to the masking strategy defined in the training script.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: LoRA (full precision base)
Max sequence length: 2048
Epochs: 2
Learning rate: 1e-05
LoRA: r=128, alpha=256

Usage

python

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "takayosh/maskver"

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.