base_model, datasets, language, license, library_name, pipeline_tag, tags

base_model	datasets	language	license	library_name	pipeline_tag	tags
Qwen/Qwen3-4B-Instruct-2507	u-10bei/structured_data_with_cot_dataset_512_v5 daichira/structured-5k-mix-sft	en	apache-2.0	transformers	text-generation	lora merged structured-output

Qwen3-4B-Instruct-2507-sft_166 (merged LoRA, multi-stage SFT)

This repository provides a merged full model fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using LoRA.

Important: This repository does NOT provide separate LoRA adapter weights. It contains merged model weights only (the adapter is not uploaded).

Training Objective

This model is fine-tuned to improve structured output accuracy (JSON / YAML / XML / TOML / CSV).

Note:

• This README focuses on the competition-required model card structure.
• If you need more training/implementation details, please refer to your training logs or scripts.

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: LoRA (adapters merged after training)
• Max sequence length: 512
• Epochs: 2
• LoRA: r=1, alpha=2

Multi-stage SFT (2 stages)

Stage 1 (YAML-focused SFT)

• Data: “to YAML” subset only (from the sources listed below)
• Learning rate: 2.0e-4

Stage 2 (XML-focused SFT)

• Data: “to XML” subset only (from the sources listed below)
• Learning rate: 1.1e-4

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "n4/Qwen3-4B-Instruct-2507-sft_166"
tok = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, device_map="auto", trust_remote_code=True)

user_query = "Please output the following information in JSON format: Name=naisy, Age=714"
messages = [{"role": "user", "content": user_query}]
prompt = tok.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512, do_sample=False)
gen_ids = out[0][inputs["input_ids"].shape[1]:]
text = tok.decode(gen_ids, skip_special_tokens=True)
print(text)

Sources & Terms (IMPORTANT)

Training data:

• u-10bei/structured_data_with_cot_dataset_512_v5
• daichira/structured-5k-mix-sft

Dataset License:

• u-10bei/structured_data_with_cot_dataset_512_v5: MIT License
• daichira/structured-5k-mix-sft: CC-BY-4.0

Compliance: Users must comply with:

• the dataset licenses above (including attribution requirements for CC-BY-4.0), and
• the base model's original terms of use (apache-2.0).

Limitations

• Structured outputs may still fail under very long, deeply nested, or ambiguous schemas.
• Validate outputs (e.g., JSON parse / XML validation) in downstream use cases.