Update README.md

README.md

@@ -8,22 +8,20 @@ license: llama3
 base_model:
 - aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct
 ---
-# Llama3 8B CPT Sahabat AI v1
-
-Sahabat AI v1 is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian languages.
-This is the card for the Llama3 8B CPT Sahabat AI v1 base model which has undergone continued pre-training from the [AI Singapore-Llama-3-8B-Sea-Lion v2.1-Instruct](https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct) model.
-
-Sahabat is Indonesian for "Close Friends."
+# Llama3 8B CPT Sahabat-AI v1
 
+**Sahabat-AI** (Indonesian language for “close friends”) is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian language and its various dialects. 
+Sahabat-AI ecosystem is co-initiated by Indonesian tech and telecommunication companies:  GoTo Group and Indosat Ooredoo Hutchison.
 
 ## Model Details
 
 ### Model Description
 
-The continued pre-training data for Llama3 8B CPT Sahabat AI v1 base model encompasses approximately 80B tokens.
+The continued pre-training data for Llama3 8B CPT Sahabat-AI v1 base model encompasses approximately 80B tokens.
 
 - **Developed by:** PT GoTo Gojek Tokopedia Tbk,  AI Singapore
 - **Funded by:** PT GoTo Gojek Tokopedia Tbk, AI Singapore
+- **Supported by:**  PT Indosat Ooredoo Hutchison
 - **Model type:** Decoder
 - **Languages:** English, Indonesian, Javanese, Sundanese
 - **License:** [Llama3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)
@@ -31,7 +29,7 @@ The continued pre-training data for Llama3 8B CPT Sahabat AI v1 base model encom
 For tokenisation, the model employs the default tokenizer used in Llama-3-8B. The model has a context length of 8192.
 
 ### Benchmark Performance
-We evaluated Llama 8B CPT Sahabat AI v1 base model on general language capabilities.
+We evaluated Llama 8B CPT Sahabat-AI v1 base model on general language capabilities.
 
 #### General Language Capabilities
 For the evaluation of general language capabilities, we employed the 
@@ -147,7 +145,7 @@ The evaluation was done **five-shot** with native prompts on a sample of 100-100
 
 ### Data
 
-Llama3 8B CPT Sahabat AI v1 base model was continued pre-trained on 50B tokens of the following data:
+Llama3 8B CPT Sahabat-AI v1 base model was continued pre-trained on 50B tokens of the following data:
 
 
 | Data Source                           | Unique Tokens (B) | Multiplier | Total Tokens (B) | Percentage (%)|
@@ -171,10 +169,10 @@ Note:
 
 ### Infrastructure
 
-Llama 8B CPT Sahabat AI v1 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
+Llama 8B CPT Sahabat-AI v1 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
 on the following hardware:
 
-| Training Details     | Llama3 8B CPT Sahabat AI v1|
+| Training Details     | Llama3 8B CPT Sahabat-AI v1|
 |----------------------|:----------------------------:|
 | Nvidia H100 80GB GPU |        32                    |
 | Training Duration    |        5 days                |
@@ -182,7 +180,7 @@ on the following hardware:
 
 ### Configuration
 
-| HyperParameter    | Llama3 8B CPT Sahabat AI v1|
+| HyperParameter    | Llama3 8B CPT Sahabat-AI v1|
 |-------------------|:----------------------------:|
 | Precision         | bfloat16                     |
 | Optimizer         | decoupled_adamw              |
@@ -192,7 +190,28 @@ on the following hardware:
 | Micro Batch Size  | 1                            |
 
 
-## The Team (by ascending alphabetical order)
+## Call for Collaboration 
+
+Sahabat-AI (Indonesian language for “close friends”) a **local open source Large Language Model (LLM) ecosystem in Indonesian language**, co-initiated by Indonesian tech and telecommunication companies:  GoTo Group and Indosat Ooredoo Hutchison. 
+Sahabat-AI ecosystem aims to empower Indonesians who want to develop AI-based services and applications using Bahasa Indonesia and its various local dialects. 
+
+We are supported by research centers and global tech experts such as AI Singapore and Tech Mahendra to train the model to gain general language understanding.
+
+We also have collaborated with key top Indonesia universities such as University of Indonesia, Gadjah Mada University, Bogor Institute of Agriculture, Bandung Institute of Technology, including top Indonesia media groups, such as Kompas Media Group and Republika to train and enrich the model in Bahasa Indonesia, ensuring optimum provision of local context and cultural relevance.
+
+We would like to invite **researchers, developers, and language enthusiasts** to actively contribute to the enhancement and expansion of Sahabat-AI. 
+Your collaborations can involve:
+- Identifying and reporting technical issues
+- Sharing pre-training, instruction, and preference data
+- Improving documentation usability
+- Proposing and implementing new model evaluation tasks and metrics 
+
+Join us in shaping the future of Sahabat-AI by sharing your expertise and insights to make these models more accessible, accurate, and versatile.
+
+You can contribute your ideas through [this form.](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)
+
+
+## The Development Team (in ascending alphabetical order)
 
 ### AI Singapore
 Chan Adwin<br>