78 lines
2.4 KiB
Markdown
78 lines
2.4 KiB
Markdown
---
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base_model: unsloth/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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---
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# dpo-qwen3_4b-cot-merged_v260302-010243
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This model is a fine-tuned version of **unsloth/Qwen3-4B-Instruct-2507** using **Supervised Fine-Tuning (SFT)** and **Direct Preference Optimization (DPO)** via the **Unsloth** library.
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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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In SFT, an adapter is trained to improve **structured output accuracy** (JSON / YAML / XML / TOML / CSV).
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Loss is applied only to the final assistant output, while intermediate reasoning (Chain-of-Thought) is masked.
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After SFT, the adapter is merged to the base model.
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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**: unsloth/Qwen3-4B-Instruct-2507
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- **Method**: SFT(Supervised Fine-Tuning) + DPO (Direct Preference Optimization)
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### SFT Training Configuration
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- **Max sequence length**: 768
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- **Epochs**: 2
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- **Learning rate**: 5e-06
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- **LoRA Config**: QLoRA(4-bit), r=128, alpha=128
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### DPO Training Configuration
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- **Epochs**: 5
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- **Learning rate**: 1e-06
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- **Beta**: 0.2
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- **Max sequence length**: 768
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- **LoRA Config**: r=64, alpha=64 (merged into base)
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- **Early Drop**: threshold=1.2
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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 = "your_id/your-repo-name"
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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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