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Llama LLM

Llama LLM screenshot

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About

Llama LLM by Meta is a state-of-the-art open-source large language model designed for various use cases. The recent Llama 3 features pretrained and instruction-fine-tuned models with 8 billion and 70 billion parameters, which significantly improves performance through efficient encoding using a vocabulary of 128K tokens. Llama 2, its predecessor, is free to use and ideal for developing chatbots, although not explicitly a chatbot itself. Llama 3.1 presents new workflows for building multi-agent applications, while LlamaCloud enables data access control essential for enterprise-level applications. Llama also provides an extensive FAQ section addressing the capabilities and comparisons with other models like ChatGPT, making it a comprehensive tool in the AI landscape.

Platform

Keywords

ai language models open-source meta llama

Task

Features

• pretrained language models

• data access control for enterprise applications

• improved performance with new tokenizer

• open-source and customizable

• supports a wide range of applications

FAQs

What can LLaMA 2 do?

LLaMA 2 is a versatile language model designed to respond in a human-like manner and can be used to develop chatbots.

Is LLaMA 2 free to use?

Yes, Llama 2 is open-source and available for both research and commercial uses.

Is LLaMA a chatbot?

LLaMA 2 isn't primarily a chatbot but a flexible LLM that developers can customize.

Is LLaMA better than ChatGPT?

Llama 2 produces more secure outputs and better performance compared to ChatGPT with GPT-3.5.

How to use LLaMA 2 in Python?

Download the model from Hugging Face, set up a Python environment, and install the required packages.

Pricing Plans

Free

• Open-source architecture

• Pretrained models

• Available for research and commercial use

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Alternatives

Llama 3.2

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PaLM-E

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Google Gemma

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Mistral 7B

A powerful open-source LLM by Mistral AI, known for its performance and flexibility across various applications.

Ollama

Run and customize large language models on multiple platforms.

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