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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 BaseRT?

BaseRT provides a powerful inference runtime for large language models, specifically tailored for Apple Silicon, enabling developers to effortlessly access models from Hugging Face, engage in local dialogue, or use an OpenAI-compatible API all through a single command-line interface. Boosted by expertly designed Metal kernels, BaseRT is engineered to excel in prefill and decoding efficiency on M-series Macs, with benchmark tests demonstrating performance that is up to 6.4 times faster in prefill tasks than llama.cpp, 3.9 times quicker than MLX, and achieving a decoding speed that surpasses competitors by 1.33 times. The BaseRT CLI is equipped to handle various tasks including model downloading, conversion, interactive chatting, serving functionalities, completion generation, benchmarking, inspection, and bundle signing. Its comprehensive server capabilities include chat interactions, text completions, embeddings, transcription services, tool calls, continuous batching, paged key-value caching, and prefix caching, while supporting models that process text, vision, and audio data. BaseRT utilizes a unique .base model format that features Q2–Q8 affine quantization, optional AWQ calibration, and signed bundles, and it can convert GGUF, Hugging Face, and MLX checkpoints seamlessly. In addition to these features, this groundbreaking runtime is specifically designed to harness the full potential of Apple Silicon, establishing itself as an indispensable resource for developers working in the AI domain. With its impressive efficiency and broad functionality, BaseRT stands out as a key innovation for the future of AI development on Apple platforms.

Media

Media

Integrations Supported

1min.AI
AI-FLOW
AI/ML API
Agenta
AnythingLLM
Batteries Included
Browser Use
Code Llama
Deasie
Fleak
Lakera
PostgresML
PromptPal
ReByte
Runpod
Unify AI

Integrations Supported

Gemma 4
Hugging Face
Mistral AI

API Availability

API Availability

Has API

Pricing Information

Free
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

Mac

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

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

Base Compute

Date Founded

2026

Company Location

Australia

Company Website

www.basecompute.co/getbasert

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 Tools

Not specified

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