Large Language Models (LLM) have gained worldwide attention and are popular in the field of natural language processing and natural language understanding. This allows researchers to help intelligent systems better and more clearly understand human language. They can mimic humans learning to read, generate text, complete code, translate language, and summarize long paragraphs. Large models are trained on large amounts of data to understand the grammar, semantics, and usage scenarios of human language. Recent breakthroughs in artificial intelligence, especially generative AI, have sparked the imagination of the public. Typical models that offer exceptional performance and extraordinary capabilities are the Llama 2 and GPT-4 .
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Meta partnered with Microsoft to launch LLaMA 2, an updated version of the popular language model LLaMa. This innovative model enables smooth understanding and generation of content in multiple languages. LLaMA 2 builds on Llama's solid foundation and undoubtedly raises the bar for multilingual capabilities. The model is licensed for research and commercial use, and will soon be accessible through the Microsoft Azure platform and Amazon SageMaker.
The main feature of Llama 2 is that it is fluent in multiple languages and is able to understand and generate texts in more than 200 languages. Removing language barriers that previously made it difficult to effectively communicate across countries and cultures, Llama 2 is now available worldwide. Second, the significant improvement of Llama 2 can be seen more from the cultural context analysis. This feature enables the model to respond more sensitively to context and the cultural subtlety and sensitivity of the user.
Llama 2 also demonstrates the extraordinary ability to use what has been learned in one language to enhance understanding and expression in other languages. The model can take advantage of the massive amounts of data it processes across multiple languages, so Llama 2 improves its ability to understand and create content in multiple languages, making it a highly flexible and effective language model.
The latest version of GPT-4 allows text and image input, while GPT 3.5 only allows ChatGPT to accept text input. Compared to previous versions, the GPT 4 model is considered more maneuverable. It has a transformer architecture and shows near-human performance due to its more reliable and creative nature.
The number of parameters in GPT-4 affects its size and complexity, making it unique. The model can efficiently process and analyze massive amounts of data. Due to the large number of parameters, GPT-4 can capture complex patterns, dependencies, and links within the data, resulting in more coherent and context-appropriate text.
The GPT-4 complex architecture is designed to interpret language in a way that closely resembles human understanding. It can utilize its extensive training data and sophisticated neural networks to recognize subtleties and contextual clues in the input text. Despite its large size and complexity, it has excellent responsiveness and guarantees seamless and fluid user interaction with GPT-4, improving its suitability in various fields.
The biggest difference between Llama 2 and GPT-4 is that Llama 2 is open source, and the purpose of making Llama 2 open source is to allow startups and other businesses to access it and adapt it to create their own AI products. Pre-trained and fine-tuned Llama 2 models range in size from 7 billion to 70 billion parameters (weighted mathematical equations for each point in a neural network). The Llama 2 research paper abstract reads: "Our model outperforms other open-source chat models on most of the benchmarks we tested, and may be a suitable alternative to closed-source models based on our human assessment of usefulness and safety." "Closed source" refers to similar language models from OpenAI and Google.
The Llama 2 model accepts 40% more training tags (words or parts of words) than the original Llama 1 model and can read and remember longer prompts—up to 4,000 marks.
Meta has a large and high-quality AI research organization and ample cash to support its work, and is always recruiting the best AI talent. The company has been working on its own large-scale language models for years, but has chosen to open source many of its models rather than charge other companies for access like OpenAI. Application developers and enterprises are often paid to access OpenAI's language models through application programming interfaces (APIs) or Microsoft cloud services in Azure.
Llama 2 has been highly rated by the open source community for its performance and flexibility. Nathan Lambert, a machine learning scientist at Hugging Face, wrote in a blog post on Tuesday: "The base model appears to be very powerful (beyond GPT-3), and the fine-tuned chat model seems to be on the same level as ChatGPT." "This is a huge leap forward for open source and a huge blow for closed-source providers, because using this model will provide more customizability and lower costs for most companies."
However, some point out that Llama may not yet be able to become a challenger to OpenAI models in all areas, such as coding. "On 'HumanEval,' it's nowhere near as good as StarCoder or many other models designed for coding," tweeted Jim Fan, senior AI scientist at Nvidia. "That being said, I have no doubt that Llama-2 will significantly improve its capabilities in terms of coding."
Qualcomm is working with Microsoft to integrate the Llama 2 model into phones, laptops and headphones starting in 2024. Given the breadth of potential innovation in Llama 2, AI experts believe it could be a game-changer for the industry.
It's worth noting that Microsoft is the largest investor in OpenAI, but supports Llama 2. It seems that Microsoft is the player who can achieve a win-win and take-all in the artificial intelligence track.
There are widespread concerns in the tech world about open source AI. The main concern is that open source may make powerful AI tools accessible to all, including bad actors like hackers and criminals. As of now, Meta requires users to agree to an acceptable use policy, which includes not using Llama 2 for false information or encouraging violence or terrorism. The Llama 2 model provides developers with guidelines for responsible use.