Update README.md

README.md

@@ -6,20 +6,20 @@ tags:
 - conversational
 ---
 
-# EZO model card
+# [EZO model card]
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/0OYFqT8kACowa9bY1EZF6.png)
 **Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-2-9b-it)
 **Authors**: Axcxept co., ltd.
 
-## Model Information
+## [Model Information]
 This model is based on Gemma-2-9B-it, enhanced with multiple tuning techniques to improve its general performance. While it excels in Japanese language tasks, it's designed to meet diverse needs globally.
 
 Gemma-2-9B-it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。日本語タスクに優れつつ、世界中の多様なニーズに応える設計となっています。
 
-### Benchmark Results
+### [Benchmark Results]
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/XyPo_1rVa_ufmV5SeLepQ.png)
 
-### Usage
+### [Usage]
 Here are some code snippets to quickly get started with the model. First, run:
 `pip install -U transformers`
 Then, copy the snippet from the relevant section for your use case.
@@ -29,7 +29,7 @@ Then, copy the snippet from the relevant section for your use case.
 `pip install -U transformers`
 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
+### [Chat Template]
 ```py
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import transformers
@@ -50,7 +50,7 @@ outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=150)
 print(tokenizer.decode(outputs[0]))
 ```
 
-### Template
+### [Template]
 ```
 <bos><start_of_turn>user
 Write a hello world program<end_of_turn>
@@ -58,10 +58,8 @@ Write a hello world program<end_of_turn>
 XXXXXX<end_of_turn><eos>
 ```
 
-### Model Data
-Information about the data used for model training and how it was processed.
-
-#### Training Dataset
+### [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。
@@ -69,7 +67,7 @@ We extracted high-quality data from Japanese Wikipedia and FineWeb to create ins
 https://huggingface.co/datasets/legacy-datasets/wikipedia
 https://huggingface.co/datasets/HuggingFaceFW/fineweb
 
-### Data Preprocessing
+#### 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。
@@ -79,9 +77,9 @@ We used a plain instruction tuning method to train the model on exemplary respon
 
 https://huggingface.co/instruction-pretrain/instruction-synthesizer
 
-### Hardware
+### [Hardware]
 A100 × 4(Running in 32h)
 
-### We are.
+### [We are.]
 [![Axcxept logo](https://cdn-uploads.huggingface.co/production/uploads/657e900beaad53ff67ba84db/8OKW86U986ywttvL2RcbG.png)](https://axcxept.com)