Update README.md

README.md

@@ -7,11 +7,11 @@ language:
 - vi
 license: llama3
 ---
-# Llama3 8B CPT SEA-LIONv2 Instruct
+# Llama3 8B CPT SEA-Lionv2.1 Instruct
 
 SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
 
-Llama3 8B CPT SEA-LIONv2 Instruct is a multilingual model which has been fine-tuned with around **100,000 English instruction-completion pairs** alongside a smaller pool of around **50,000 instruction-completion pairs** from other ASEAN languages, such as Indonesian, Thai and Vietnamese.
+Llama3 8B CPT SEA-Lionv2.1 Instruct is a multilingual model which has been fine-tuned with around **100,000 English instruction-completion pairs** alongside a smaller pool of around **50,000 instruction-completion pairs** from other ASEAN languages, such as Indonesian, Thai and Vietnamese.
 These instructions have been carefully curated and rewritten to ensure the model was trained on truly open, commercially permissive and high quality datasets.
 
 SEA-LION stands for _Southeast Asian Languages In One Network_.
@@ -25,12 +25,12 @@ SEA-LION stands for _Southeast Asian Languages In One Network_.
 ## Model Details
 
 ### Model Description
-We performed instruction tuning in English and also in ASEAN languages such as Indonesian, Thai and Vietnamese on our [continued pre-trained Llama3 CPT 8B SEA-LIONv2](https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2-base), a decoder model using the Llama3 architecture, to create Llama3 8B SEA-LIONv2 Instruct.
+We performed instruction tuning in English and also in ASEAN languages such as Indonesian, Thai and Vietnamese on our [continued pre-trained Llama3 CPT 8B SEA-Lionv2](https://huggingface.co/aisingapore/llama3-8b-cpt-SEA-Lionv2.1-base), a decoder model using the Llama3 architecture, to create Llama3 8B SEA-Lionv2.1 Instruct.
 
 The model has a context length of 8192.
 
 ### Benchmark Performance
-We evaluated Llama3 8B SEA-LIONv2 Instruct on both general language capabilities and instruction-following capabilities.
+We evaluated Llama3 8B SEA-Lionv2.1 Instruct on both general language capabilities and instruction-following capabilities.
 
 #### General Language Capabilities
 For the evaluation of general language capabilities, we employed the [BHASA evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
@@ -42,7 +42,7 @@ The evaluation was done zero-shot with native prompts and only a sample of 100-1
 
 
 #### Instruction-following Capabilities
-Since LLaMa3 8B SEA-LIONv2 is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, [IFEval](https://arxiv.org/abs/2311.07911) and [MT-Bench](https://arxiv.org/abs/2306.05685).
+Since LLaMa3 8B SEA-Lionv2.1 is an instruction-following model, we also evaluated it on instruction-following capabilities with two datasets, [IFEval](https://arxiv.org/abs/2311.07911) and [MT-Bench](https://arxiv.org/abs/2306.05685).
 
 As these two datasets were originally in English, the linguists and native speakers in the team worked together to filter, localize and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
 
@@ -56,7 +56,7 @@ IFEval evaluates a model's ability to adhere to constraints provided in the prom
 MT-Bench evaluates a model's ability to engage in multi-turn (2 turns) conversations and respond in ways that align with human needs. We use `gpt-4-1106-preview` as the judge model and compare against `gpt-3.5-turbo-0125` as the baseline model. The metric used is the weighted win rate against the baseline model (i.e. average win rate across each category (Math, Reasoning, STEM, Humanities, Roleplay, Writing, Extraction)). A tie is given a score of 0.5.
 
 
-For more details on Llama3 8B CPT SEA-LIONv2 Instruct benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/
+For more details on Llama3 8B CPT SEA-Lionv2.1 Instruct benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/
 
 
 ### Usage
@@ -67,7 +67,7 @@ SEA-LION can be run using the 🤗 Transformers library
 import transformers
 import torch
 
-model_id = "aisingapore/llama3-8b-cpt-sea-lionv2-instruct"
+model_id = "aisingapore/llama3-8b-cpt-SEA-Lionv2.1-instruct"
 
 pipeline = transformers.pipeline(
     "text-generation",
@@ -100,10 +100,10 @@ Current SEA-LION models, including this commercially permissive release, have no
 
 ## Technical Specifications
 ### Fine-Tuning Details
-The Llama3 8B CPT SEA-LIONv2 Instruct was fine-tuned using 8x A100-40GB using parameter efficient fine tuning in the form of LoRA.
+The Llama3 8B CPT SEA-Lionv2.1 Instruct was fine-tuned using 8x A100-40GB using parameter efficient fine tuning in the form of LoRA.
 
 ## Data
-Llama3 8B CPT SEA-LIONv2 Instruct was trained on a wide range of instructions that were manually and stringently verified by our team. A large portion of the effort was dedicated to ensuring that each instruction-completion pair that the model sees is of high quality and any errors were corrected and rewritten by native speakers or else dropped from our mix.
+Llama3 8B CPT SEA-Lionv2.1 Instruct was trained on a wide range of instructions that were manually and stringently verified by our team. A large portion of the effort was dedicated to ensuring that each instruction-completion pair that the model sees is of high quality and any errors were corrected and rewritten by native speakers or else dropped from our mix.
 
 In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source.