Open source language models (LLMs) have the potential to catch up to models like GPT-4V and Google's Gemini, but it requires a collaborative effort from the open source community. Here are a few ways open source LLMs can catch up:

1. Community contributions: Open source LLMs benefit from contributions from a wide range of developers and researchers. Encouraging community involvement can lead to faster model improvements and innovations.

2. Data collection and curation: High-quality datasets are crucial for training LLMs. By organizing efforts to collect and curate relevant data, open source projects can improve the performance and accuracy of their models.

3. Optimization techniques: Researchers can develop novel optimization techniques specifically tailored for open source LLMs. These techniques can help improve model training efficiency and reduce computational requirements.

4. Transfer learning and pre-training: Leveraging pre-training techniques like transfer learning can help open source LLMs benefit from large-scale models like GPT-4V and Gemini. By fine-tuning these models on specific tasks, open source LLMs can reach higher levels of performance.

5. Collaboration with industry: Collaborating with industry experts and researchers can provide valuable insights and resources for open source LLM development. This collaboration can help bridge the gap between open source projects and advanced proprietary models.

It's important to note that catching up to models like GPT-4V and Gemini might require considerable time and resources. However, with continuous effort, community collaboration, and innovation, open source LLMs have the potential to narrow the gap and offer competitive alternatives.

Gemini is Google’s attempt to build a general-purpose AI program that can rival OpenAI’s GPT-4 model, which powers a paid version of ChatGPT. Demis Hassabis, the Google executive overseeing the project, told employees during a recent companywide meeting that the program would become available later this year, according to people who heard the remarks.

#artificialinteligence

Large Language Models (LLM ) are nothing more than machine learning models that are capable of performing a variety of Natural Language Processing (NLP) tasks. They are trained on huge data sets so that they are able to answer questions, generate their own content, properly classify it, summarize it or translate it into foreign languages.🍹

The appearance of their next generations of such models is proof of the rapid progress in the development of artificial intelligence.

It is estimated that the size of large language models has increased tenfold each year in recent years. As their size and, consequently, the level of complexity increase, so do their capabilities.

This is perfectly visible on the example of ChatGPT, which in its previous version was not so precise.🎺

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It couldn’t handle even longer written forms, it was often repetitive and as a result it didn’t deliver the values ​​expected by the end user. These imperfections have been largely eliminated in the currently available version of the model, but it is still far from perfect. However, this does not change the fact that its capabilities are impressive. Also impressive is the work done by the algorithms thanks to which it is able to surprise users and change our reality.

To build a model to support ChatGPT, OpenAI used a Microsoft-provided tens of millions of dollars worth of supercomputer , which at the time was among the top five most powerful machines in the world.

Thanks in advance 😭

ChatGPT can do wonders!🌿

You can translate code from one programming language to another.

Prompt:

"Translate this code from JavaScript to Python {Enter code}"

Note: All AI-generated content must be carefully vetted. AI can “hallucinate” and generate plausible facts or content that is entirely false yet utterly convincing. Your knowledge as the expert is critical for fact-checking and editing.🌿