Contest running about LLM context-awareness

Research & Innovation 🧮

🥁 Drumroll! This week, Gemini (Generalized Multimodal Intelligence Network Interface) made its debut – undoubtedly the LLM everyone is talking about right now. But I don't want to delve into what all the media is covering. What it is, how it works, and its differences compared to GPT – you'll surely find that in other places. This time, I want to focus on the goal users have in mind (or at least they should) when using these models and how Gemini changes the landscape.

Let's think of Gemini as a big brain, a network of models capable of receiving questions from any type of input data (meaning it's multimodal). Through the interaction of a family of models, with a success rate of 90%, Gemini represents the advancement of other state-of-the-art efforts that have been working on similar tasks.

Recently, some articles highlight that for the use of LLMs to solve specific tasks, it's important to consider the following aspects:

💻 Computational Cost: The more efficient the model, the more resources need to be utilized.

🔍 Context Access: A structure aiding in discerning knowledge and matching user questions with agent responses is crucial.

🌐 Cultural Nuances: LLMs lack awareness of cultural nuances, limiting their understanding beyond an English-centric worldview.

🛠️ Orchestration Framework: To unlock the full potential of LLMs, a well-designed orchestration framework is essential.

🌍 Impact on Inequality: GenAI could potentially worsen inequality, and policymakers' reactions will be key.

🚗Context-Driven: Enhance LLMs' performance and precision by infusing gold context during training, fostering peak potential and reducing memory bias.

Additionally, developers recognize challenges such as data security, scalability, and the scarcity of experts in this field. Therefore, we now comprehend the delay with Gemini, attributed to meticulous care for model security and rigorous testing. All aspects mentioned are part of the state-of-the-art in independent solutions. However, it is crucial to highlight to what extent Gemini already complies with these aspects. At the moment, we can say that concerning the size of the models adjusting to diverse devices, with some advocating for democratization and tailoring models to the client's size (and the devices they use), this opens up a broader perspective.

Food for thought🧐: I envision in the near future a recipe for blending models in such a way that we can meet everyone's requirements without unfairly diminishing the overall impact.

Even though it's a bit late, this article inspired me for the previous text, enjoy it! 🌟

The repo! 👾

When it comes to new models, there is a lighter alternative to Mistral for Ollama users called DeepSeek LLM. Additionally, you can also try DeepSeek Coder for more code-explanation guides that you can incorporate from this public repo into an agent in CodeGPT Plus. By the way, DeepSeek is now available in the VS Extension. Simply follow the instructions and enjoy!

New at CodeGPT 🎁

BETA Playground early adopters 🥳!

The Beta version of CodeGPT Plus Playground is just what we've been waiting for—I'm even counting myself as part of the support team, haha. It comes with a host of new features, including the long-awaited increase in the number of interactions per agent!

To gain access, you just need to fill out the following form, and you'll be able to enjoy the benefits in your current plan. 🚀