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Model: demimomi/dpo-qwen-cot-merged Source: Original Platform
2026-05-05 08:04:51 +08:00
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+---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- dpo_train_brushed_v4_balanced.json
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- dpo
+- unsloth
+- qwen
+- alignment
+---
+
+# 東京大学 松尾・岩澤研究室　大規模言語モデル　応用講座2025-2026
+
+## Author and Acknowledgments
+
+- **Author:** Toshiki Demizu (出水 利樹) — GitHub/Hugging Face ID: [@demimomi](https://huggingface.co/demimomi)  
+- **Affiliation:** ソフトバンク株式会社、MONET Technologies株式会社
+- **Course:** Large Language Model Development Lecture Advanced (Winter 2025－2026)  
+- **Participants:** 3800名参加 
+
+## メインコンペ(2026年2月2日～3月2日)
+
+- **状況:**
+
+  2026年2月8日現在 293位（現時点で497人が提出） 0.70044点
+
+  2026年2月11日現在 261位（現時点で646人が提出）0.73407点
+  https://huggingface.co/demimomi/demimomi44taomax-qwen3-4b-structured-output-lora
+
+  T4（TPU）だと日次Limitにすぐ達するため、A100（GPU）にて学習/推論コードを実施。
+
+- **ルール:**
+
+基準点：0.7　※コード脳死で回すだけでは超えられない
+
+　１Google Colabで実行可能なモデル・実装であること 
+
+　２評価は StructEval（Text）のみを使用
+
+　３提出物は推論結果JSONとHugging Face上のモデルURL
+
+　４運営指定モデル・データのみ使用可
+
+　５Omnicampusに提出すると自動採点・順位付け
+
+# ＜demimomi-max44-qwen3-4b-dpo-qwen-cot-merged（ 0.70044点版）＞
+
+## model
+
+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 Objective
+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.
+
+## Training Configuration
+- **Base model**: Qwen/Qwen3-4B-Instruct-2507
+- **Method**: DPO (Direct Preference Optimization)
+- **Epochs**: 2
+- **Learning rate**: 1e-06
+- **Beta**: 0.05
+- **Max sequence length**: 1536
+- **LoRA Config**: r=8, alpha=16 (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 = "demimomi/dpo-qwen-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)
+
+* **Training Data**: [dpo_train_brushed_v4_balanced.json]
+* **License**: MIT License. (As per dataset terms).
+* **Compliance**: Users must follow the original base model's license terms.