System Prompts Removed: The model is trained without system prompts to match the inference environment where they are unavailable.
CoT Removed (Direct Output): Chain-of-Thought (reasoning steps) and "Output:" markers were physically removed from the training data.
Assistant-Only Loss: The model is trained to output the structured data immediately after the user prompt, with loss applied only to the output.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: QLoRA (4-bit)
Max sequence length: 512
Epochs: 1
Learning rate: 1e-06
LoRA: r=64, alpha=128

Usage

python

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

base_model_name = "Qwen/Qwen3-4B-Instruct-2507"
adapter_name = "your_id/your-repo" # Replace with your HF hub path

tokenizer = AutoTokenizer.from_pretrained(base_model_name)
model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter_name)

# Inference Example
messages = [
    {"role": "user", "content": "Convert this text to JSON: ..."}
]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

# The model will output JSON immediately
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Sources & Terms (IMPORTANT)

Training data: u-10bei/structured_data_with_cot_dataset_512_v2

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.

mark-22/LoRA_dataclean_2

Qwen3-4B Structured Output LoRA (No-CoT / Strict-JSON)

Training Objective

Training Configuration

Usage

Sources & Terms (IMPORTANT)

mark-22/
LoRA_dataclean_2