Best SLM on Outpost for OCR

The best play here would be to use an SLM with multi-modal capabilities. Currently Outposts offers the g4dn series of GPN enabled instances.

Here is a working solution from aws-samples where we used a local SLM on Outposts for GenAI inference. This could be implemented on Outposts Rack. https://github.com/aws-samples/gen-ai-at-the-edge/tree/main

I've not tested an Arabica fluent SLM but list here -> https://huggingface.co/blog/silma-ai/arabic-llm-models-list

Correct me if I am wrong, but I think the question is referring to the Arabica Python library that streamlines the exploratory analysis of time-series text data, not the Arabic language.

With that lens I would recommend deploying Amazon SageMaker with Hugging Face's DistilBERT Advantages: Lightweight model (66M parameters) that runs efficiently on AWS Outpost hardware Can be deployed using SageMaker Neo for optimization on specific Outpost hardware Compatible with Arabica for time-series text analysis Can be integrated with Amazon Textract for OCR capabilities

Two alternative options: AWS Neuron-optimized BERT-Base Optimized for AWS Inferentia chips (available on AWS Outposts with the EC2 Inf1 instance types) 110M parameters - larger than DistilBERT but with better performance Works with Arabica through standard Python interfaces Can be paired with open-source Tesseract OCR or Amazon Textract Hugging Face's BERT-Tiny Ultra-compact model (4.4M parameters) Excellent for resource-constrained environments Easily integrates with Arabica's Python ecosystem Can use AWS SDK to connect with Amazon Rekognition for OCR

Implementation Approach: Deploy the selected model on AWS Outpost using SageMaker or container services Install Arabica and necessary OCR libraries in the same environment Create a processing pipeline that: Uses OCR to extract text from images/documents Feeds extracted text to Arabica for time-series analysis Leverages the small language model for classification or generation tasks

OCR Integration Options: Amazon Textract: AWS-native solution with high accuracy (requires network connectivity to AWS Region) Tesseract OCR: Open-source solution that can run entirely on Outpost PaddleOCR: Lightweight OCR framework with good performance on edge devices

The combination of DistilBERT with Arabica and an appropriate OCR solution provides an efficient system for processing and analyzing time-series text data extracted from images or documents on AWS Outpost.

For OCR capabilities on AWS Outposts, you have a few options to consider.

If you're looking for a Small Language Model (SLM) that can run on AWS Outposts and handle OCR including Arabic text, you could deploy an open-source OCR solution like Tesseract on an EC2 instance within your Outpost rack. Tesseract is relatively lightweight and can be integrated into your workflow with minimal setup - you would just need to install it on your device and use the Python package.

For the language model component, you could deploy an SLM using frameworks like Ollama on a G4 family EC2 instance on your Outpost. This approach allows you to conduct edge inferencing locally while maintaining data residency requirements. Alternatively, you could use ModelBuilder with the SageMaker SDK to deploy to a local endpoint on your EC2 instance running at the edge.

If you need more advanced document processing capabilities beyond basic OCR, you might consider a hybrid approach where you use a local OCR solution on your Outpost for initial text extraction, and then leverage more sophisticated language understanding capabilities either locally or through secure connections to AWS services like Amazon Bedrock (if your architecture and data residency requirements permit).

For multilingual support including Arabic, ensure that the OCR solution and language model you choose have strong capabilities in that language. Some open-source models have varying degrees of support for different languages, so testing with your specific use case would be advisable.
Sources
Implement RAG while meeting data residency requirements using AWS hybrid and edge services | AWS Machine Learning Blog
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