Llama
META’s answer to ChatGPT, Llama is an open-source large language model (LLM). Available for both research and commercial use free of charge, it includes model weights and starting code for pretrained and fine-tuned models alike. These range from 7 billion to 70 billion parameters, indicating the model’s complexity and potential for detailed understanding and response generation.
The pretrained models have been trained on an impressive 2 trillion tokens, with a context length double that of its predecessor, Llama 1. As for the fine-tuned models, including Llama Chat and Code Llama, they have been trained with over 1 million human annotations — enhancing their accuracy and reliability.
Llama demonstrates superior performance over other open-source language models in several external benchmarks. Said benchmarks test various capabilities like reasoning, coding, language proficiency, and knowledge retention — and these advancements indicate that Llama could potentially offer more accurate and contextually relevant responses, making it a valuable tool for developers and researchers.
The model is divided into two main components: Llama Chat and Code Llama. The former was pretrained on publicly available online data sources and further refined using instruction datasets and human annotations. This component of Llama is geared towards natural language understanding and response.
On the other hand, Code Llama specializes in code generation. It is trained on 500 billion tokens of code and supports several common programming languages like Python, C++, Java, PHP, Typescript, C#, and Bash. This diversity in language support underlines its potential utility in software development and related fields.
Finally, Llama is backed by a broad range of global partners and supporters who believe in Meta’s open approach to AI. These supporters come from various sectors, including tech, academia, and policy, and see the benefits of an open platform for AI development.
What are the key features? ⭐
- A range of models: Llama offers a variety of models with varying complexities, ranging from 7 billion to a whopping 70 billion parameters. This means the model can understand and generate language with incredible depth and nuance.
- Extensive training: The training behind Llama is monumental. The pretrained models have been fed over 2 trillion tokens, which means that Llama can comprehend and respond to a wide array of topics with a high degree of accuracy and relevance.
- Specialized components: Llama has two specialized components: Llama Chat for natural language understanding and response and Code Llama for code generation.
- Good in benchmarks: In external benchmarks, Llama has shown superior performance over other open-source language models in areas like reasoning, language proficiency, and knowledge retention.
- Global support: Beyond META (Facebook), Llama is also backed by a diverse range of global partners, from tech giants to academic institutions. This broad support ensures that Llama remains an open and accessible platform.
Who is it for? 🤔
Examples of what you can use it for 💭
- Build chatbots that are not only more accurate in understanding customer requests but also provide more relevant responses
- With its Code Llama component, it can assist in writing and debugging code
- Get help generating ideas, drafting content, and even providing suggestions for improvement
- As an educational tool, Llama can assist in tutoring students in various subjects
- Researchers can use Llama to sift through large volumes of text and data
Pros & Cons ⚖️
- Powerful AND open-source LLM
- Lets you create your own chatbot
- Also helps with writing, coding, answering, tutoring, and more
- Most users still support OpenAI's GPT model
