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Model: masachika/qwen3-4b-dpo-cot-merged
Source: Original Platform

README.md

@@ -0,0 +1,80 @@
+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- u-10bei/dpo-dataset-qwen-cot
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- dpo
+- unsloth
+- qwen
+- alignment
+- two-stage-training
+---
+
+# qwen3-4b-2507-dpo-cot-merged
+
+This model is the result of **two-stage fine-tuning**:
+
+1. **SFT (Supervised Fine-Tuning)**: Training on structured output datasets
+2. **DPO (Direct Preference Optimization)**: Alignment using preference data
+
+This repository contains the **full-merged 16-bit weights**. No adapter loading is required.
+
+## Training Pipeline
+
+### Stage 1: SFT
+- **Base model**: Qwen/Qwen3-4B-Instruct-2507
+- **SFT adapter**: masachika/qwen3-4b-Instruct-2507-structured-output-lora
+- **Objective**: Learn to generate structured outputs (JSON, YAML, XML, TOML, CSV)
+
+### Stage 2: DPO (This model)
+- **Starting point**: SFT-trained model (merged)
+- **Method**: Direct Preference Optimization
+- **Dataset**: u-10bei/dpo-dataset-qwen-cot
+- **Objective**: Align responses with preferred outputs, improve reasoning quality
+
+## Training Configuration (DPO Stage)
+- **Epochs**: 2
+- **Learning rate**: 3e-07
+- **Beta**: 0.2
+- **Max sequence length**: 2048
+- **LoRA Config**: r=64, alpha=128 (merged into base)
+
+## Usage
+Since this is a merged model, you can use it directly with `transformers`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "masachika/qwen3-4b-dpo-cot-merged"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+# Test inference
+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 (IMPORTANT)
+
+* **SFT Base**: masachika/qwen3-4b-Instruct-2507-structured-output-lora
+* **DPO Training Data**: u-10bei/dpo-dataset-qwen-cot
+* **License**: MIT License (as per dataset terms)
+* **Compliance**: Users must follow the original base model's license terms