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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 LTM-2-mini?
LTM-2-mini is designed to manage a context of 100 million tokens, which is roughly equivalent to about 10 million lines of code or approximately 750 full-length novels. This model utilizes a sequence-dimension algorithm that proves to be around 1000 times more economical per decoded token compared to the attention mechanism employed by Llama 3.1 405B when operating within the same 100 million token context window. Additionally, the difference in memory requirements is even more pronounced; running Llama 3.1 405B with a 100 million token context requires an impressive 638 H100 GPUs per user just to sustain a single 100 million token key-value cache. In stark contrast, LTM-2-mini only needs a tiny fraction of the high-bandwidth memory available in one H100 GPU for the equivalent context, showcasing its remarkable efficiency. This significant advantage positions LTM-2-mini as an attractive choice for applications that require extensive context processing while minimizing resource usage. Moreover, the ability to efficiently handle such large contexts opens the door for innovative applications across various fields.
Integrations Supported
AI4Chat
Agenta
Airtrain
BrandRank.AI
Code Llama
Coginiti
Cyte
Fireworks AI
Fleak
GMTech
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
Customer Service / Support
Not specified
Customer Service / Support
Not specified
Training Options
Documentation Hub
Training Options
Not specified
Company Facts
Organization Name
Meta
Date Founded
2004
Company Location
United States
Company Website
ai.meta.com/llama/
Company Facts
Organization Name
Magic AI
Date Founded
2022
Company Location
United States
Company Website
magic.dev/
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 Coding Models
Not specified
AI Models
Not specified
Large Language Models
Not specified
Small Language Models
Not specified