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Model: n4/Qwen3-4B-Instruct-2507-sft_166
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- u-10bei/structured_data_with_cot_dataset_512_v5
+- daichira/structured-5k-mix-sft
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- 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
+
+```python
+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.