Update README.md

README.md

@@ -45,6 +45,43 @@ print(output)
 ## Dataset (fine-tuning)
 - Ichikara instruction [[Web Page](https://liat-aip.sakura.ne.jp/wp/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF%E4%BD%9C%E6%88%90/llm%E3%81%AE%E3%81%9F%E3%82%81%E3%81%AE%E6%97%A5%E6%9C%AC%E8%AA%9E%E3%82%A4%E3%83%B3%E3%82%B9%E3%83%88%E3%83%A9%E3%82%AF%E3%82%B7%E3%83%A7%E3%83%B3%E3%83%87%E3%83%BC%E3%82%BF-%E5%85%AC%E9%96%8B/)], [[Ppaer](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/A6-3.pdf)]
 
+## Performance
+
+**Stockmark Business Questions**
+
+Dataset: https://huggingface.co/datasets/stockmark/business-questions
+
+| model | accuracy |
+|:---:|:---:|
+|stockmark-100b-instruct| 0.90 |
+|stockmark-13b-instruct| 0.80 |
+|GPT-3.5-turbo[^1]| 0.42 |
+
+[^1]: 0613
+
+**Japanese Vicuna QA Benchmark**
+
+We exclud categories that require calculation and coding, and use remaining 60 questions for evaluation.
+
+GitHub: https://github.com/ku-nlp/ja-vicuna-qa-benchmark
+
+| model | average score |
+|:---:|:---:|
+|stockmark-100b-instruct| 5.97 |
+|tokyotech-llm/Swallow-70b-instruct-hf| 5.59 |
+|GPT-3.5 (text-davinci-003)| 5.08 |
+
+**Inference speed**
+
+| model | time [s] for genrating 100 characters in Japanese |
+|:---:|:---:|
+|stockmark-100b-instruct[^2]| 1.86 |
+| gpt-3.5-turbo | 2.15 |
+| gpt-4-turbo | 5.48 |
+|tokyotech-llm/Swallow-70b-instruct-hf[^2]| 2.22 |
+
+[^2]: We measured the time using AWS Inferentia2.
+
 ## License
 [MIT](https://opensource.org/licenses/MIT)