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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 Aquarium?

Aquarium's cutting-edge embedding technology adeptly identifies critical performance issues in your model while linking you to the necessary data for resolution. By leveraging neural network embeddings, you can reap the rewards of advanced analytics without the headaches of infrastructure management or troubleshooting embedding models. This platform allows you to seamlessly uncover the most urgent patterns of failure within your datasets. Furthermore, it offers insights into the nuanced long tail of edge cases, helping you determine which challenges to prioritize first. You can sift through large volumes of unlabeled data to identify atypical scenarios with ease. The incorporation of few-shot learning technology enables the swift initiation of new classes with minimal examples. The larger your dataset grows, the more substantial the value we can deliver. Aquarium is crafted to effectively scale with datasets comprising hundreds of millions of data points. Moreover, we provide dedicated solutions engineering resources, routine customer success meetings, and comprehensive user training to help our clients fully leverage our offerings. For organizations with privacy concerns, we also feature an anonymous mode, ensuring that you can utilize Aquarium without compromising sensitive information, thereby placing a strong emphasis on security. In conclusion, with Aquarium, you can significantly boost your model's performance while safeguarding the integrity of your data, ultimately fostering a more efficient and secure analytical environment.

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Integrations Supported

Amazon Bedrock
Atomic Chat
Clore.ai
CoSpaceGPT
Diaflow
Featherless
GPT for Work
GPTZero
Llama 4 Scout
Mastra
Mirai
PostgresML
Revere
Skott
Snack Prompt
Teradata VantageCloud
Unsloth
Yonoo
amazee.ai
kluster.ai

Integrations Supported

API Availability

API Availability

Pricing Information

Pricing not provided

Pricing Information

$1,250 per month
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub
Online Training

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

www.llama.com

Company Facts

Organization Name

Aquarium

Company Website

www.aquariumlearning.com

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 Observability

Not specified

Embedding Models

Not specified

Machine Learning

Not specified

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