AXCXEPT
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EZO-Qwen2.5-72B-Instruct

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 ---
+license: apache-2.0
+language:
+- ja
+- en
+pipeline_tag: text-generation
+base_model: Qwen/Qwen2.5-72B-Instruct
+tags:
+- chat
 ---
+# AXCXEPT/EZO-Qwen2.5-72B-Instruct
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/_9uZ9yI6dI7V3FqDED_C3.png)
+## Introduction
+This model is based on Qwen/Qwen2.5-72B-Instruct and has undergone multiple tuning to improve overall performance from the Base model.
+Although it excels in Japanese language tasks, it is designed to meet a variety of global needs.
+#### In the Japanese MT Bench using gpt-4o as the evaluator, the inference performance of this model with 4-bit quantization achieved a score higher than that of gpt-4-turbo.
+Qwen/Qwen2.5-72B-Instructをベースに複数のチューニングを施し、Baseモデルから総合的なパフォーマンスを向上させたモデルです。
+日本語タスクを得意としますが、グローバルな多様なニーズに対応できるように設計されています。
+gpt-4oを評価器としたJapanese MT Benchにおいて、4ビット量子化を用いた本モデルの推論能力はgpt-4-turboを超えるスコアを達成いたしました。
+## [Benchmark Results]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/xzmMXfzF1JRFXrgNAEDQM.png)
+## [Usage]
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+```bash
+pip install bitsandbytes transformers accelerate
+```
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_name = "AXCXEPT/EZO-Qwen2.5-72B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+ model_name,
+ torch_dtype=torch.bfloat16,
+ device_map="auto",
+ load_in_4bit=True
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+prompt = "仕事の熱意を取り戻すためのアイデアを5つ挙げてください。"
+messages = [
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+ messages,
+ tokenize=False,
+ add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt")
+#if you don't use "load_in_4bit", you should do "model_inputs = tokenizer([text], return_tensors="pt").to(model.device)"
+generated_ids = model.generate(
+ **model_inputs,
+ max_new_tokens=512
+)
+generated_ids = [
+ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+#### Training Dataset]
+We extracted high-quality data from Japanese Wikipedia and FineWeb to create instruction data. Our innovative training approach allows for performance improvements across various languages and domains, making the model suitable for global use despite its focus on Japanese data.
+日本語のWikiデータおよび、FineWebから良質なデータのみを抽出し、Instructionデータを作成しました。このモデルでは日本語に特化させていますが、世界中のどんなユースケースでも利用可能なアプローチです。
+https://huggingface.co/datasets/legacy-datasets/wikipedia
+https://huggingface.co/datasets/HuggingFaceFW/fineweb
+#### Data Preprocessing
+We used a plain instruction tuning method to train the model on exemplary responses. This approach enhances the model's ability to understand and generate high-quality responses across various languages and contexts.
+プレインストラクトチューニング手法を用いて、模範的回答を学習させました。この手法により、モデルは様々な言語やコンテキストにおいて高品質な応答を理解し生成する能力が向上しています。
+#### Implementation Information
+[Pre-Instruction Training]
+https://huggingface.co/instruction-pretrain/instruction-synthesizer
+### [Disclaimer]
+このモデルは研究開発のみを目的として提供されるものであり、実験的なプロトタイプとみなされるべきモデルです。
+商業的な使用やミッションクリティカルな環境への配備を意図したものではありません。
+本モデルの使用は、使用者の責任において行われるものとし、その性能および結果は保証されません。
+Axcxept株式会社は、直接的、間接的、特別、偶発的、結果的な損害、または本モデルの使用から生じるいかなる損失に対しても、得られた結果にかかわらず、一切の責任を負いません。
+利用者は、本モデルの使用に伴うリスクを十分に理解し、自己の判断で使用するものとします。
+### [Hardware]
+A100 × 4(Running in 32h)
+### [謝辞]
+本ベースモデルを開発してくださったGoogle社ならびに当該チームの開発者の方々、また自動評価の手法を提供してくださった多数の方々に感謝と尊敬の意を表します。
+### Company:
+Axcxept co., ltd.
+[![Axcxept logo](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/8OKW86U986ywttvL2RcbG.png)](https://axcxept.com)