Update README.md

README.md

@@ -13,11 +13,11 @@ language:
 - km
 - ta
 ---
-# SEA-LION
+# SEA-LION-v1-3B
 
 SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
 The size of the models range from 3 billion to 7 billion parameters.
-This is the card for the SEA-LION 3B base model.
+This is the card for SEA-LION-v1-3B.
 
 SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
 
@@ -29,11 +29,11 @@ SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
 The SEA-LION model is a significant leap forward in the field of Natural Language Processing,
 specifically trained to understand the SEA regional context.
 
-SEA-LION is built on the robust MPT architecture and has a vocabulary size of 256K.
+SEA-LION-v1-3B is built on the robust MPT architecture and has a vocabulary size of 256K.
 
 For tokenization, the model employs our custom SEABPETokenizer, which is specially tailored for SEA languages, ensuring optimal model performance.
 
-The training data for SEA-LION encompasses 980B tokens.
+The training data for SEA-LION-v1-3B encompasses 980B tokens.
 
 - **Developed by:** Products Pillar, AI Singapore
 - **Funded by:** Singapore NRF
@@ -43,7 +43,7 @@ The training data for SEA-LION encompasses 980B tokens.
 
 ### Performance Benchmarks
 
-SEA-LION has an average performance on general tasks in English (as measured by Hugging Face's LLM Leaderboard):
+SEA-LION-v1-3B has an average performance on general tasks in English (as measured by Hugging Face's LLM Leaderboard):
 
 | Model       | ARC   | HellaSwag | MMLU  | TruthfulQA | Average |
 |-------------|:-----:|:---------:|:-----:|:----------:|:-------:|
@@ -53,7 +53,7 @@ SEA-LION has an average performance on general tasks in English (as measured by
 
 ### Data
 
-SEA-LION was trained on 980B tokens of the following data:
+SEA-LION-v1-3B was trained on 980B tokens of the following data:
 
 | Data Source               | Unique Tokens | Multiplier | Total Tokens | Percentage |
 |---------------------------|:-------------:|:----------:|:------------:|:----------:|
@@ -79,10 +79,10 @@ SEA-LION was trained on 980B tokens of the following data:
 
 ### Infrastructure
 
-SEA-LION was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
+SEA-LION-v1-3B was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
 on the following hardware:
 
-| Training Details     | SEA-LION 3B  |
+| Training Details     | SEA-LION-v1-3B  |
 |----------------------|:------------:|
 | AWS EC2 p4d.24xlarge | 30 instances |
 | Nvidia A100 40GB GPU | 240          |
@@ -91,7 +91,7 @@ on the following hardware:
 
 ### Configuration
 
-| HyperParameter    | SEA-LION 3B        |
+| HyperParameter    | SEA-LION-v1-3B       |
 |-------------------|:------------------:|
 | Precision         | bfloat16           |
 | Optimizer         | decoupled_adamw    |
@@ -105,9 +105,9 @@ on the following hardware:
 
 ### Model Architecture and Objective
 
-SEA-LION is a decoder model using the MPT architecture.
+SEA-LION-v1-3B is a decoder model using the MPT architecture.
 
-| Parameter       | SEA-LION 3B |
+| Parameter       | SEA-LION-v1-3B |
 |-----------------|:-----------:|
 | Layers          | 32          |
 | d_model         | 2560        |