---
base_model: unsloth/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/structured_data_with_cot_dataset_512_v2
- u-10bei/dpo-dataset-qwen-cot
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- sft
- dpo
- unsloth
- qwen
- alignment
- lora
---

# koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_03

This model is a fine-tuned version of **unsloth/Qwen3-4B-Instruct-2507** using a **two-stage training pipeline: SFT (Supervised Fine-Tuning) followed by DPO (Direct Preference Optimization)** via the **Unsloth** library.

This repository contains the **full-merged 16-bit weights**. No adapter loading is required.

## Training Pipeline

### Stage 1: SFT (Supervised Fine-Tuning)
The base model was first fine-tuned with SFT to learn structured output generation and Chain-of-Thought reasoning.

- **Dataset**: [u-10bei/structured_data_with_cot_dataset_512_v2](https://huggingface.co/datasets/u-10bei/structured_data_with_cot_dataset_512_v2)
- **LoRA Config**: r=64, alpha=128, dropout=0
- **Target modules**: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
- **Learning rate**: 1e-6
- **Epochs**: 1
- **Max sequence length**: 512
- **Loss strategy**: Assistant-only loss with CoT masking (Output marker)
- **SFT LoRA adapter**: [koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_03](https://huggingface.co/koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_03)

### Stage 2: DPO (Direct Preference Optimization)
The SFT-trained LoRA was merged into the base model, then a new LoRA adapter was applied for DPO training to further align the model with preferred outputs.

- **Dataset**: [u-10bei/dpo-dataset-qwen-cot](https://huggingface.co/datasets/u-10bei/dpo-dataset-qwen-cot)
- **LoRA Config**: r=8, alpha=16, dropout=0
- **Learning rate**: 1e-07
- **Epochs**: 1
- **Beta**: 0.1
- **Max sequence length**: 1024
- **Optimizer**: AdamW 8-bit

## Usage
Since this is a merged model, you can use it directly with `transformers`.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_03"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

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

* **SFT Data**: [u-10bei/structured_data_with_cot_dataset_512_v2](https://huggingface.co/datasets/u-10bei/structured_data_with_cot_dataset_512_v2)
* **DPO Data**: [u-10bei/dpo-dataset-qwen-cot](https://huggingface.co/datasets/u-10bei/dpo-dataset-qwen-cot)
* **License**: Apache 2.0 (as per base model terms)
* **Compliance**: Users must follow the original base model's license terms.