--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards {} --- # Model Card for Model ID gemma-2-9b-it quantized with imatrix containing a lot of Japanese text 日本語テキストを多く含むimatrixで量子化されたgemma-2-9b-it ## Model Details It is known that using imatrix when quantizing a model for llama.cpp improves performance. However, imatrix is often created only from English text. In cases where a model is used in languages other than English, wouldn't it be better to create an imatrix by mixing text in other languages? This page confirms the effectiveness of multilingual imatrix. モデルをllama.cpp用に量子化する際にimatrixを使うと性能が向上する事が知られています。 しかし、imatrixは英語テキストのみから作成されている事が多いです。英語以外の言語を使ってモデルを使用するケースでは他の言語のテキストも混ぜてimatrixを作成した方がよいのではないでしょうか? 本ページは多言語版imatrixの有効性を確かめました。 ### Model Description ![wiki.test.raw_perplexity_score.png](wiki.test.raw_perplexity_score.png) ![wiki.test.raw_perplexity_score.png](wiki.test.raw_perplexity_score.png) #### Terminology Bartowski model Bartowski is a person who has quantized many models and contributed to the community. He creates imatrix from English-only data[calibration_datav3.txt](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8). Imatrix-jpn-test model Comparing the performance of models created with the multilingual version of Imatrix - **Developed by:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]