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Sahabat-AI-Gemma2-9B-Instruct

Parameters

9.2B

Context Length

8K

Modality

Text

Architecture

Dense

KV Cache Rate

336 KB/tok

License

Gemma-Community

Release Date

14 Nov 2024

Knowledge Cutoff

Mar 2024

API Pricing (per 1M)

Self-hosted only

Hardware & System Requirements

Precision

Native

Context

Workload Memory: 9.41 GB

INT4 · 8K context

Base Weights: 4.28 GB

KV Cache: 2.63 GB

Runtime & Graphs: 2.50 GB

Minimum Required Hardware

1x NVIDIA L4 24GB

9.4 / 21.6 GB usable

1x RTX 3060 12GB

9.4 / 10.8 GB usable

Apple M4 24GB

9.4 / 18.0 GB usable

Determine requirements for your specific workload:

Open in VRAM Calculator

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 3.6k · Context: 8K · Vocab: 256kx 42 layersRMSNormPre-AttentionMulti-Head Attention16Q / 8KV heads · SW: 4.1kHead dim: 256+RMSNormPre-FFNFeed-Forward NetworkGated GELUIntermediate: 14.3k+Final RMSNormOutput Logits

Evaluation Benchmarks

No evaluation benchmarks for Sahabat-AI-Gemma2-9B-Instruct available.

Rankings

Overall Rank

-

Coding Rank

-

About Sahabat-AI-Gemma2-9B-Instruct

Sahabat-AI-Gemma2-9B-Instruct is an instruction-tuned Indonesian language model by GoTo, Indosat, and AI Singapore for enterprise chatbots and digital assistants. It excels in standard Bahasa Indonesia, Javanese, and Sundanese conversational tasks.

Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

16

Key-Value Heads

8

Attention Head Dimension

256

Position Embedding

Absolute Position Embedding

RoPE Theta

10,000

Sliding Window Attention

Yes

Sliding Window Size

4,096

Normalization

RMS Normalization

Activation Function

Gated GELU

Dimensions

Base Precision

BF16

Hidden Dimension Size

3,584

Number of Layers

42

FFN Intermediate Size (Dense)

14,336

Tokenizer

Vocabulary Size

256,000

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.


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