AXCXEPT
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llm-jp-3-3.7b-instruct-EZO-Common

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: apache-2.0
+language:
+- en
+- ja
 library_name: transformers
+pipeline_tag: text-generation
+tags:
+- conversational
 ---
+# [EZO model card]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/0OYFqT8kACowa9bY1EZF6.png)
+## [Model Information]
+This model is based on llm-jp/llm-jp-3-3.7b-instruct, enhanced with multiple tuning techniques to improve its general performance.
+llm-jp/llm-jp-3-3.7b-instructをベースとして、複数のチューニング手法を採用のうえ、汎用的に性能を向上させたモデルです。
+### [Benchmark Results]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/XyPo_1rVa_ufmV5SeLepQ.png)
+**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-9b-it)
+This model is based on llm-jp/llm-jp-3-3.7b-instruct and follows the terms of use (apache-2.0) of llm-jp/llm-jp-3-3.7b-instruct.
+このモデルはllm-jp/llm-jp-3-3.7b-instructをベースにしており、llm-jp/llm-jp-3-3.7b-instructの利用規約(apache-2.0)を踏襲します。
+### [Usage]
+Here are some code snippets to quickly get started with the model. First, run:
+- torch>=2.3.0
+- transformers>=4.40.1
+- tokenizers>=0.19.1
+- accelerate>=0.29.3
+- flash-attn>=2.5.8
+Then, copy the snippet from the relevant section for your use case.
+以下に、モデルの実行を素早く開始するためのコードスニペットをいくつか紹介します。
+まず、
+- torch>=2.3.0
+- transformers>=4.40.1
+- tokenizers>=0.19.1
+- accelerate>=0.29.3
+- flash-attn>=2.5.8
+を実行し、使用例に関連するセクションのスニペットをコピーしてください。
+### [Chat Template]
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+model_id = "AXCXEPT/llm-jp-3-3.7b-instruct-EZO-Common"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
+chat = [
+ {"role": "system", "content": "以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。"},
+ {"role": "user", "content": "自然言語処理とは何か"},
+]
+tokenized_input = tokenizer.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_tensors="pt").to(model.device)
+with torch.no_grad():
+ output = model.generate(
+ tokenized_input,
+ max_new_tokens=100,
+ do_sample=True,
+ top_p=0.95,
+ temperature=0.7,
+ repetition_penalty=1.05,
+ )[0]
+print(tokenizer.decode(output))
+```
+### [Model Data]
+#### 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��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 × 8(Running in 4h)
+### [謝辞]
+We would like to express our gratitude and respect to LLM-jp and the developers of this team for developing this base model, and to the many people who provided the automated evaluation methodology.
+本ベースモデルを開発してくださったLLM-jp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。
+### [We are.]
+[![Axcxept logo](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/8OKW86U986ywttvL2RcbG.png)](https://axcxept.com)