MaLLaM-3B
Parameters
3B
Context Length
4K
Modality
Text
Architecture
Dense
License
Apache-2.0
Release Date
15 Jan 2024
Knowledge Cutoff
Jan 2024
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
4,096 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 MaLLaM-3B available.
Rankings
Overall Rank
-
Coding Rank
-
About MaLLaM-3B
MaLLaM-3B (Malaysia Large Language Model) is a foundational 3 billion parameter dense model engineered specifically for the Malaysian linguistic context. Developed from scratch by Malaysia AI and Mesolitica, the model addresses the scarcity of high-quality local language representations by leveraging a curated dataset of 90 billion tokens. This training corpus comprises 349GB of diverse Malaysian digital artifacts, including government documents, local news, literature from the Dewan Bahasa Pustaka, and colloquial social media exchanges. By utilizing a custom-trained Byte Pair Encoding (BPE) tokenizer, the model captures unique Malaysian idioms, slang, and cultural references that are often diluted in English-centric foundational models.
Technically, MaLLaM-3B adopts the Mistral transformer-based decoder-only architecture, which facilitates efficient inference and high performance relative to its parameter count. The model utilizes Grouped-Query Attention (GQA) to optimize the KV cache, thereby reducing memory overhead during sequence generation. It implements the SwiGLU activation function and RMSNorm for stable and accelerated convergence during pre-training. For position encoding, the model employs Rotary Position Embeddings (RoPE), enabling it to maintain precise token relationships within its standard 4096-token context window.
Designed primarily for edge deployment and localized applications, MaLLaM-3B is optimized for environments where low-latency text generation and bilingual proficiency in Bahasa Malaysia and English are required. Its compact architecture makes it suitable for integration into mobile applications, localized chatbots, and on-premise document processing systems. Released under the Apache 2.0 license, the model provides an open-weights foundation for researchers and developers to build downstream tasks such as sentiment analysis, summarization, and instruction-following assistants tailored for the Malaysian demographic.
Technical Specifications
Attention
Attention Structure
Multi-Head Attention
Attention Heads
32
Key-Value Heads
8
Attention Head Dimension
100
Position Embedding
Absolute Position Embedding
RoPE Theta
10,000
Sliding Window Attention
Yes
Sliding Window Size
4,096
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
SwigLU
Dimensions
Hidden Dimension Size
3,200
Number of Layers
26
FFN Intermediate Size (Dense)
8,640
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
32,000
About MaLLaM
Malaysian Large Language Model (MaLLaM) is an open-source language model family developed to support Bahasa Malaysia and English. The model is trained on Malaysian text data including local news, literature, and digital content. It is designed to process Malaysian linguistic nuances and cultural context, available in multiple parameter sizes for different hardware deployments.
Other MaLLaM Models
- No related models available