74 lines
2.3 KiB
Markdown
74 lines
2.3 KiB
Markdown
---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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adapter: ykawasaki/qwen3-4b-structured-output-lora-v12
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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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---
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# ＜qwen3-4b-dpo-qwen-cot-merged-v7＞
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This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
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The basemodel was merged with an adapter **ykawasaki/qwen3-4b-structured-output-lora-v12** beforehand.
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This repository contains the **full-merged 16-bit weights**. No adapter loading is required.
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## Training Objective
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This model has been optimized using DPO to align its responses with preferred outputs, focusing on improving reasoning (Chain-of-Thought) and structured response quality based on the provided preference dataset.
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## Training Configuration
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- **Base model**: Qwen/Qwen3-4B-Instruct-2507
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- **Adapter**: ykawasaki/qwen3-4b-structured-output-lora-v12
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- **Method**: DPO (Direct Preference Optimization)
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- **Epochs**: 3
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- **Learning rate**: 1e-07
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- **Beta**: 0.1
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- **Max sequence length**: 1024
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- **LoRA Config**: r=16, alpha=32 (merged into base)
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- **LoRA dropout**: 0.05
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- **rsLoRA**: False
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- **DoRA**: True
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- **Per device batch size**: 16
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- **Gradient accumulation**: 8
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- **Learning rate scheduler**: SchedulerType.COSINE
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## Usage
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Since this is a merged model, you can use it directly with `transformers`.
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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 = "ykawasaki/qwen3-4b-dpo-qwen-cot-merged-v7"
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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.float16,
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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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