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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

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

Llama Stack is an innovative modular framework aimed at simplifying the creation of applications that utilize Meta's Llama language models. It features a client-server architecture with adaptable configurations, giving developers the ability to combine various providers for essential components like inference, memory, agents, telemetry, and evaluations. This framework comes with pre-configured distributions optimized for a range of deployment scenarios, facilitating smooth transitions from local development to live production settings. Developers can engage with the Llama Stack server through client SDKs that support numerous programming languages, including Python, Node.js, Swift, and Kotlin. In addition, comprehensive documentation and sample applications are made available to help users efficiently construct and deploy applications based on the Llama framework. The combination of these resources aims to empower developers to build robust, scalable applications with ease.

API Access

Has API No 

API Access

Has API No 

Screenshots View All

No images available

Screenshots View All

Integrations

Axolotl Yes 
CoSpaceGPT Yes 
DataChain Yes 
DockClaw Yes 
FalkorDB Yes 
Firecrawl Yes 
Fleak Yes 
Jitterbit Yes 
Kodosumi Yes 
Lakera Yes 
Mastra AI Yes 
Plano Yes 
Ragas Yes 
SheetMagic Yes 
Teneo.AI Yes 
Tiger Data Yes 
Tune AI Yes 
UndetectedGPT Yes 
Undrstnd Yes 
Yonoo Yes 

Integrations

Axolotl No 
CoSpaceGPT No 
DataChain No 
DockClaw No 
FalkorDB No 
Firecrawl No 
Fleak No 
Jitterbit No 
Kodosumi No 
Lakera No 
Mastra AI No 
Plano No 
Ragas No 
SheetMagic No 
Teneo.AI No 
Tiger Data No 
Tune AI No 
UndetectedGPT No 
Undrstnd No 
Yonoo No 

Pricing Details

No price information available.
Free Trial No 
Free Version No 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Deployment

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

Deployment

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

Customer Support

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

Customer Support

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

Types of Training

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

Types of Training

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

Vendor Details

Company Name

Meta

Founded

2004

Country

United States

Website

www.llama.com

Vendor Details

Company Name

Meta

Founded

2004

Country

United States

Website

github.com/meta-llama/llama-stack

Product Features

Alternatives

Alternatives

Gemini Reviews

Gemini

Google
Alpaca Reviews

Alpaca

Stanford Center for Research on Foundation Models (CRFM)