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Ratings and Reviews 0 Ratings
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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.
Integrations Supported
1min.AI
AI-FLOW
AI/ML API
Agenta
AnythingLLM
Batteries Included
Browser Use
Code Llama
Deasie
Fleak
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