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What is Llama 3.2?

The newest version of the open-source AI framework, which can be customized and utilized across different platforms, is available in several configurations: 1B, 3B, 11B, and 90B, while still offering the option to use Llama 3.1. Llama 3.2 includes a selection of large language models (LLMs) that are pretrained and fine-tuned specifically for multilingual text processing in 1B and 3B sizes, whereas the 11B and 90B models support both text and image inputs, generating text outputs. This latest release empowers users to build highly effective applications that cater to specific requirements. For applications running directly on devices, such as summarizing conversations or managing calendars, the 1B or 3B models are excellent selections. On the other hand, the 11B and 90B models are particularly suited for tasks involving images, allowing users to manipulate existing pictures or glean further insights from images in their surroundings. Ultimately, this broad spectrum of models opens the door for developers to experiment with creative applications across a wide array of fields, enhancing the potential for innovation and impact.

What is Llama?

Llama, a leading-edge foundational large language model developed by Meta AI, is designed to assist researchers in expanding the frontiers of artificial intelligence research. By offering streamlined yet powerful models like Llama, even those with limited resources can access advanced tools, thereby enhancing inclusivity in this fast-paced and ever-evolving field. The development of more compact foundational models, such as Llama, proves beneficial in the realm of large language models since they require considerably less computational power and resources, which allows for the exploration of novel approaches, validation of existing studies, and examination of potential new applications. These models harness vast amounts of unlabeled data, rendering them particularly effective for fine-tuning across diverse tasks. We are introducing Llama in various sizes, including 7B, 13B, 33B, and 65B parameters, each supported by a comprehensive model card that details our development methodology while maintaining our dedication to Responsible AI practices. By providing these resources, we seek to empower a wider array of researchers to actively participate in and drive forward the developments in the field of AI. Ultimately, our goal is to foster an environment where innovation thrives and collaboration flourishes.

Media

Media

No images available

Integrations Supported

AICamp
Agenta
BlueFlame AI
Bolna
BrandRank.AI
DataChain
Decopy AI
Evertune
Featherless
Fleak
Gopher
Graydient AI
LFM2.5
Meta AI
Microsoft Foundry Agent Service
Ragas
SurePath AI
Tune AI
fullmoon

Integrations Supported

AICamp
Agenta
BlueFlame AI
Bolna
BrandRank.AI
DataChain
Decopy AI
Evertune
Featherless
Fleak
Gopher
Graydient AI
LFM2.5
Meta AI
Microsoft Foundry Agent Service
Ragas
SurePath AI
Tune AI
fullmoon

API Availability

API Availability

Pricing Information

Free
Open source
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Not specified

Customer Service / Support

Not specified

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

www.llama.com

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

www.llama.com

Categories and Features

AI Models

Not specified

Embedding Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Models

Not specified

Embedding Models

Not specified

Foundation Models

Not specified

Generative AI

Not specified

Large Language Models

Not specified

Small Language Models

Not specified

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