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What is Llama 2?

We are excited to unveil the latest version of our open-source large language model, which includes model weights and initial code for the pretrained and fine-tuned Llama language models, ranging from 7 billion to 70 billion parameters. The Llama 2 pretrained models have been crafted using a remarkable 2 trillion tokens and boast double the context length compared to the first iteration, Llama 1. Additionally, the fine-tuned models have been refined through the insights gained from over 1 million human annotations. Llama 2 showcases outstanding performance compared to various other open-source language models across a wide array of external benchmarks, particularly excelling in reasoning, coding abilities, proficiency, and knowledge assessments. For its training, Llama 2 leveraged publicly available online data sources, while the fine-tuned variant, Llama-2-chat, integrates publicly accessible instruction datasets alongside the extensive human annotations mentioned earlier. Our project is backed by a robust coalition of global stakeholders who are passionate about our open approach to AI, including companies that have offered valuable early feedback and are eager to collaborate with us on Llama 2. The enthusiasm surrounding Llama 2 not only highlights its advancements but also marks a significant transformation in the collaborative development and application of AI technologies. This collective effort underscores the potential for innovation that can emerge when the community comes together to share resources and insights.

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.

Media

Media

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Integrations Supported

AI/ML API
Alpaca
Anyscale
BlueFlame AI
Code Llama
DataChain
Deasie
Decopy AI
Ema
Kiin
LM Studio
Literal AI
Lunary
Meta AI
ModelOp
Preamble
Prompt Security
SurePath AI
Tune AI
Unify AI

API Availability

API Availability

Pricing Information

Free
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

ai.meta.com/llama/

Company Facts

Organization Name

Google DeepMind

Company Location

United States

Company Website

arxiv.org/abs/2203.15556

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

Large Language Models

Not specified

Open-Weight AI Models

Not specified

Small Language Models

Not specified

Categories and Features

AI Models

Not specified

Large Language Models

Not specified

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