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Model: nakotsuko13/qwen3-4b-nako13-dpo-qwen-cot-merged
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- u-10bei/dpo-dataset-qwen-cot
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- dpo
+- unsloth
+- qwen
+- alignment
+- structured-output
+---
+
+# ＜qwen3-4b-nako13-dpo-qwen-cot-merged＞
+
+This model is a high-performance variant of **Qwen/Qwen3-4B-Instruct-2507**, optimized for precise structured data generation.
+It was developed through a **two-stage fine-tuning process** to ensure both high knowledge density and strict output formatting.
+
+## Training Process
+1. **Stage 1: SFT (Supervised Fine-Tuning)**
+   - **Base Model**: Qwen/Qwen3-4B-Instruct-2507
+   - **Adapter**: [nakotsuko13/qwen3-4b-nako13-structured-output-lora](https://huggingface.co/nakotsuko13/qwen3-4b-nako13-structured-output-lora)
+   - **Focus**: Trained on 16,500+ samples to master JSON, XML, CSV, and YAML structures.
+
+2. **Stage 2: DPO (Direct Preference Optimization)**
+   - **Dataset**: u-10bei/dpo-dataset-qwen-cot
+   - **Focus**: Optimized to eliminate conversational filler and provide direct, raw structured outputs.
+
+## Training Configuration (DPO)
+- **Method**: DPO (Direct Preference Optimization)
+- **Epochs**: 1
+- **Learning rate**: 5e-07
+- **Beta**: 0.01
+- **Max sequence length**: 1024
+- **LoRA Config**: r=64, alpha=128 (Merged into final weights)
+
+## Usage
+This is a **full-merged 16-bit model**. It can be used directly with standard `transformers` or `vLLM`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = nakotsuko13/qwen3-4b-nako13-dpo-qwen-cot-merged
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Test inference
+prompt = "Your question here"
+inputs = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0]))
+
+```
+
+## Sources & License (IMPORTANT)
+
+* **Training Data**: [u-10bei/dpo-dataset-qwen-cot]
+* **License**: MIT License. (As per dataset terms).
+* **Compliance**: Users must follow the original base model's license terms.