100 lines
3.1 KiB
Markdown
100 lines
3.1 KiB
Markdown
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---
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base_model: Qwen/Qwen3-4B-Instruct-2507
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datasets:
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- dpo_train_brushed_v4_balanced.json
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- dpo
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- unsloth
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- qwen
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- alignment
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---
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# 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
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## Author and Acknowledgments
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- **Author:** Toshiki Demizu (出水 利樹) — GitHub/Hugging Face ID: [@demimomi](https://huggingface.co/demimomi)
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- **Affiliation:** ソフトバンク株式会社、MONET Technologies株式会社
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- **Course:** Large Language Model Development Lecture Advanced (Winter 2025－2026)
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- **Participants:** 3800名参加
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## メインコンペ(2026年2月2日～3月2日)
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- **状況:**
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2026年2月8日現在 293位（現時点で497人が提出） 0.70044点
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2026年2月11日現在 261位（現時点で646人が提出）0.73407点
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https://huggingface.co/demimomi/demimomi44taomax-qwen3-4b-structured-output-lora
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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。
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- **ルール:**
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基準点：0.7 ※コード脳死で回すだけでは超えられない
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１Google Colabで実行可能なモデル・実装であること
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２評価は StructEval（Text）のみを使用
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３提出物は推論結果JSONとHugging Face上のモデルURL
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４運営指定モデル・データのみ使用可
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５Omnicampusに提出すると自動採点・順位付け
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# ＜demimomi-max44-qwen3-4b-dpo-qwen-cot-merged（ 0.70044点版）＞
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## model
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This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
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This repository contains the **full-merged 16-bit weights**. No adapter loading is required.
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## Training Objective
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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.
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## Training Configuration
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- **Base model**: Qwen/Qwen3-4B-Instruct-2507
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- **Method**: DPO (Direct Preference Optimization)
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- **Epochs**: 2
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- **Learning rate**: 1e-06
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- **Beta**: 0.05
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- **Max sequence length**: 1536
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- **LoRA Config**: r=8, alpha=16 (merged into base)
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## Usage
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Since this is a merged model, you can use it directly with `transformers`.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "demimomi/dpo-qwen-cot-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16,
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device_map="auto"
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)
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# Test inference
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prompt = "Your question here"
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inputs = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=512)
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print(tokenizer.decode(outputs[0]))
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```
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## Sources & License (IMPORTANT)
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* **Training Data**: [dpo_train_brushed_v4_balanced.json]
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* **License**: MIT License. (As per dataset terms).
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* **Compliance**: Users must follow the original base model's license terms.
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