Change v1.0 to v1 (#1)

- Change v1.0 to v1 (354ab0d7b5faba36786f31f1e773ed92107b545f)


Co-authored-by: Kilian Tep <kiliangoto@users.noreply.huggingface.co>

README.md

@@ -12,7 +12,7 @@ license: gemma
 
 Sahabat AI is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian languages.
 
-Gemma2 9B CPT Sahabat AI v1.0 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**, as well as **129,000 instruction-completion pairs in English**.
+Gemma2 9B CPT Sahabat AI v1 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**, as well as **129,000 instruction-completion pairs in English**.
 
 Sahabat is Indonesian for "Close Friends"
 
@@ -25,12 +25,12 @@ Sahabat is Indonesian for "Close Friends"
 ## Model Details
 
 ### Model Description
-We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Gemma2 9B CPT Sahabat AI v1.0](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT Sahabat AI v1.0 Instruct.
+We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Gemma2 9B CPT Sahabat AI v1](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT Sahabat AI v1 Instruct.
 
 For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.
 
 ### Benchmark Performance
-We evaluated Gemma2 9B CPT Sahabat AI v1.0 Instruct on general language capabilities.
+We evaluated Gemma2 9B CPT Sahabat AI v1 Instruct on general language capabilities.
 
 #### General Language Capabilities
 For the evaluation of general language capabilities, we employed the 
@@ -47,7 +47,7 @@ The evaluation was done **zero-shot** with native prompts on a sample of 100-100
 
 
 ### Usage
-Gemma2 9B CPT Sahabat AI v1.0 Instruct can be run using the 🤗 Transformers library 
+Gemma2 9B CPT Sahabat AI v1 Instruct can be run using the 🤗 Transformers library 
 ```python
 # Please use transformers==4.45.2
 
@@ -83,10 +83,10 @@ Current Sahabat AI models, including this commercially permissive release, have
 
 ## Technical Specifications
 ### Fine-Tuning Details
-Gemma2 9B CPT Sahabat AI v1.0 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
+Gemma2 9B CPT Sahabat AI v1 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.
 
 ## Data
-Gemma2 9B CPT Sahabat AI v1.0 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source. 
+Gemma2 9B CPT Sahabat AI v1 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source. 
 
 ## Call for Contributions
 We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of Sahabat. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Indonesian languages. Join us in shaping the future of Sahabat by sharing your expertise and insights to make these models more accessible, accurate, and versatile.