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Model: koguma-ai/sft-dpo-qwen-cot-merged0207_unsloth_03
Source: Original Platform

README.md

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+---
+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.