Sahabat-AI-Llama3-8B-Instruct
Parameters
8B
Context Length
8K
Modality
Text
Architecture
Dense
License
Llama-3.1-Community
Release Date
14 Nov 2024
Knowledge Cutoff
Mar 2023
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
8,192 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
No evaluation benchmarks for Sahabat-AI-Llama3-8B-Instruct available.
Rankings
Overall Rank
-
Coding Rank
-
About Sahabat-AI-Llama3-8B-Instruct
Sahabat-AI-Llama3-8B-Instruct is a specialized large language model developed through a collaboration between GoTo Group and Indosat Ooredoo Hutchison. This model is constructed using a continued pre-training (CPT) approach on the Meta Llama 3 architecture, specifically optimized to reflect the linguistic patterns and cultural context of Indonesia. By incorporating a significant corpus of Indonesian text and regional languages such as Javanese and Sundanese, the model provides localized language processing capabilities that account for regional idioms and social contexts.
The technical framework is a dense, decoder-only Transformer architecture comprising 32 layers and a hidden dimension of 4096. It employs Grouped Query Attention (GQA) with 32 query heads and 8 key-value heads to improve inference efficiency. The model utilizes Rotary Positional Embeddings (RoPE) for sequence modeling and SwiGLU activation functions within its feed-forward layers. Training was facilitated by the NVIDIA NeMo framework, allowing the weights to be refined on a dataset of approximately 50 billion tokens, followed by supervised fine-tuning on hundreds of thousands of instruction-completion pairs.
This instruction-tuned variant is designed for high-quality interactions in both formal and informal Indonesian. It addresses specific cultural sensitivities and linguistic variations that are often missing in general-purpose global models. Primary applications include automated customer support for the Indonesian market, localized content synthesis, and technical assistance within the regional digital ecosystem. The model is compatible with the Transformers library and optimized for deployment on standardized accelerated computing infrastructure.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
8
Attention Head Dimension
128
Position Embedding
Absolute Position Embedding
RoPE Theta
500,000
Sliding Window Attention
No
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
4,096
Number of Layers
32
FFN Intermediate Size (Dense)
14,336
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
128,256
About Sahabat-AI
Sahabat-AI is an Indonesian language model family co-initiated by GoTo and Indosat Ooredoo Hutchison. Developed with AI Singapore and NVIDIA, it is a collection of models (based on Gemma 2 and Llama 3) specifically optimized for Bahasa Indonesia and regional languages like Javanese and Sundanese.