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