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Model: kedumerikugame/dpo-qwen-cot-merged
Source: Original Platform

README.md

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+---
+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
+---
+
+# ＜【課題】ここは自分で記入して下さい＞
+
+This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
+
+This repository contains the full-merged 16-bit weights. No adapter loading is required.
+
+## Training Configuration
+- Base model: Qwen/Qwen3-4B-Instruct-2507
+- Method: DPO (Direct Preference Optimization)
+- Epochs: 1
+- Learning rate: 1e-07
+- Beta: 0.1
+- Max sequence length: 1024
+- LoRA Config: r=8, alpha=16 (merged into base)
+
+## Usage
+
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    import torch
+
+    model_id = "your_id/your-repo-name"
+
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.float16,
+        device_map="auto"
+    )
+
+    prompt = "Your question here"
+
+    messages = [
+        {"role": "user", "content": prompt}
+    ]
+
+    input_ids = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=True,
+        return_tensors="pt",
+    )
+
+    input_ids = input_ids.to(model.device)
+
+    outputs = model.generate(
+        input_ids=input_ids,
+        max_new_tokens=512,
+    )
+
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+## Sources & License
+
+* Training Data: [u-10bei/dpo-dataset-qwen-cot]
+* License: MIT License (as per dataset terms)
+* Compliance: Follow base model license terms