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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.

What is Chinchilla?

Chinchilla represents a cutting-edge language model that operates within a compute budget similar to Gopher while boasting 70 billion parameters and utilizing four times the amount of training data. This model consistently outperforms Gopher (which has 280 billion parameters), along with other significant models like GPT-3 (175 billion), Jurassic-1 (178 billion), and Megatron-Turing NLG (530 billion) across a diverse range of evaluation tasks. Furthermore, Chinchilla’s innovative design enables it to consume considerably less computational power during both fine-tuning and inference stages, enhancing its practicality in real-world applications. Impressively, Chinchilla achieves an average accuracy of 67.5% on the MMLU benchmark, representing a notable improvement of over 7% compared to Gopher, and highlighting its advanced capabilities in the language modeling domain. As a result, Chinchilla not only stands out for its high performance but also sets a new standard for efficiency and effectiveness among language models. Its exceptional results solidify its position as a frontrunner in the evolving landscape of artificial intelligence.

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Integrations Supported

AiAssistWorks
Amazon SageMaker Unified Studio
AnyAPI
Athina AI
Axolotl
Batteries Included
Clore.ai
CompactifAI
Fleak
Jitterbit
Kodosumi
LLaMA-Factory
Lakera
Microsoft Foundry Agent Service
NeoAnalyst.ai
Scout
SurePath AI
Unframe
kluster.ai

Integrations Supported

Google Stitch

API Availability

API Availability

Pricing Information

Pricing not provided

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

Google DeepMind

Company Location

United States

Company Website

arxiv.org/abs/2203.15556

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

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

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