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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

The Gemma family consists of advanced, lightweight models developed using the same innovative research and technology as the Gemini models. These cutting-edge models are equipped with robust security features that promote responsible and trustworthy AI applications, achieved through carefully curated data sets and thorough refinements. Notably, Gemma models excel in their various sizes—2B, 7B, 9B, and 27B—often exceeding the performance of some larger open models. With the introduction of Keras 3.0, users can experience effortless integration with JAX, TensorFlow, and PyTorch, providing flexibility in framework selection based on specific tasks. Designed for peak performance and remarkable efficiency, Gemma 2 is specifically optimized for rapid inference across a range of hardware platforms. Furthermore, the Gemma family includes diverse models that cater to distinct use cases, ensuring they adapt effectively to user requirements. These lightweight language models feature a decoder and have been trained on an extensive array of textual data, programming code, and mathematical concepts, which enhances their versatility and utility in various applications.

Description

The Qualcomm Cloud AI SDK serves as a robust software suite aimed at enhancing the performance of trained deep learning models for efficient inference on Qualcomm Cloud AI 100 accelerators. It accommodates a diverse array of AI frameworks like TensorFlow, PyTorch, and ONNX, which empowers developers to compile, optimize, and execute models with ease. Offering tools for onboarding, fine-tuning, and deploying models, the SDK streamlines the entire process from preparation to production rollout. In addition, it includes valuable resources such as model recipes, tutorials, and sample code to support developers in speeding up their AI projects. This ensures a seamless integration with existing infrastructures, promoting scalable and efficient AI inference solutions within cloud settings. By utilizing the Cloud AI SDK, developers are positioned to significantly boost the performance and effectiveness of their AI-driven applications, ultimately leading to more innovative solutions in the field.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

Hugging Face Yes 
PyTorch Yes 
TensorFlow Yes 
AiAssistWorks Yes 
Amazon Web Services (AWS) No 
C Yes 
C++ Yes 
Cirrascale No 
Docker No 
EndoAim No 
F# Yes 
Gemini Enterprise Agent Platform Yes 
Google Cloud Platform Yes 
Kotlin Yes 
Nebius Token Factory Yes 
Ollama Yes 
Python Yes 
R Yes 
Ruby Yes 
SQL Yes 

Integrations

Hugging Face Yes 
PyTorch Yes 
TensorFlow Yes 
AiAssistWorks No 
Amazon Web Services (AWS) Yes 
C No 
C++ No 
Cirrascale Yes 
Docker Yes 
EndoAim Yes 
F# No 
Gemini Enterprise Agent Platform No 
Google Cloud Platform No 
Kotlin No 
Nebius Token Factory No 
Ollama No 
Python No 
R No 
Ruby No 
SQL No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Deployment

Web-Based Yes 
On-Premises No 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux No 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support Yes 

Customer Support

Business Hours Yes 
Live Rep (24/7) No 
Online Support Yes 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars Yes 
Live Training (Online) Yes 
In Person Yes 

Vendor Details

Company Name

Google

Country

United States

Website

ai.google.dev/gemma

Vendor Details

Company Name

Qualcomm

Website

www.qualcomm.com/developer/software/cloud-ai-sdk

Product Features

Deep Learning

Convolutional Neural Networks No 
Document Classification No 
Image Segmentation No 
ML Algorithm Library No 
Model Training No 
Neural Network Modeling No 
Self-Learning No 
Visualization No 

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