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ease
features
design
support

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Description

Llama (Large Language Model Meta AI) stands as a cutting-edge foundational large language model aimed at helping researchers push the boundaries of their work within this area of artificial intelligence. By providing smaller yet highly effective models like Llama, the research community can benefit even if they lack extensive infrastructure, thus promoting greater accessibility in this dynamic and rapidly evolving domain. Creating smaller foundational models such as Llama is advantageous in the landscape of large language models, as it demands significantly reduced computational power and resources, facilitating the testing of innovative methods, confirming existing research, and investigating new applications. These foundational models leverage extensive unlabeled datasets, making them exceptionally suitable for fine-tuning across a range of tasks. We are offering Llama in multiple sizes (7B, 13B, 33B, and 65B parameters), accompanied by a detailed Llama model card that outlines our development process while adhering to our commitment to Responsible AI principles. By making these resources available, we aim to empower a broader segment of the research community to engage with and contribute to advancements in AI.

Description

The Universal Sentence Encoder (USE) transforms text into high-dimensional vectors that are useful for a range of applications, including text classification, semantic similarity, and clustering. It provides two distinct model types: one leveraging the Transformer architecture and another utilizing a Deep Averaging Network (DAN), which helps to balance accuracy and computational efficiency effectively. The Transformer-based variant generates context-sensitive embeddings by analyzing the entire input sequence at once, while the DAN variant creates embeddings by averaging the individual word embeddings, which are then processed through a feedforward neural network. These generated embeddings not only support rapid semantic similarity assessments but also improve the performance of various downstream tasks, even with limited supervised training data. Additionally, the USE can be easily accessed through TensorFlow Hub, making it simple to incorporate into diverse applications. This accessibility enhances its appeal to developers looking to implement advanced natural language processing techniques seamlessly.

API Access

Has API

API Access

Has API

Screenshots View All

No images available

Screenshots View All

Integrations

AI/ML API
Admix
AnythingLLM
BlueFlame AI
Cake AI
Chatbot Arena
Clore.ai
Decompute Blackbird
Deep Infra
Entry Point AI
Klee
Lakera
Llama 4 Scout
NVIDIA Llama Nemotron
Oumi
Parasail
PostgresML
Ragas
SectorFlow
Skott

Integrations

AI/ML API
Admix
AnythingLLM
BlueFlame AI
Cake AI
Chatbot Arena
Clore.ai
Decompute Blackbird
Deep Infra
Entry Point AI
Klee
Lakera
Llama 4 Scout
NVIDIA Llama Nemotron
Oumi
Parasail
PostgresML
Ragas
SectorFlow
Skott

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Meta

Founded

2004

Country

United States

Website

www.llama.com

Vendor Details

Company Name

Tensorflow

Founded

2015

Country

United States

Website

www.tensorflow.org/hub/tutorials/semantic_similarity_with_tf_hub_universal_encoder

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