MaLLaM-7B
Parameters
7B
Context Length
4.096K
Modality
Text
Architecture
Dense
License
Apache-2.0
Release Date
15 Jan 2024
Knowledge Cutoff
-
Technical Specifications
Attention Structure
Multi-Head Attention
Hidden Dimension Size
-
Number of Layers
-
Attention Heads
-
Key-Value Heads
-
Activation Function
-
Normalization
-
Position Embedding
Absolute Position Embedding
System Requirements
VRAM requirements for different quantization methods and context sizes
MaLLaM-7B
MaLLaM-7B is a 7 billion parameter language model for Malaysian language tasks including text generation and summarization. Trained on Malaysian government documents, news, and social media, it covers regional variations of Bahasa Malaysia. Released under the Apache 2.0 license.
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
Evaluation Benchmarks
No evaluation benchmarks for MaLLaM-7B available.
Rankings
Overall Rank
-
Coding Rank
-
GPU Requirements
Full Calculator
Choose the quantization method for model weights
Context Size: 1,024 tokens