LLM-Powered Robot
In this project, I focused on implementing small open-source models, such as Zephir-7b, on single-board computers like the Raspberry Pi. This setup enabled the creation of a robot controlled by an LLM. Working with such compact models presents numerous challenges; one significant issue is the reduced level of intelligence compared to larger models like GPT-4. To address this, the project required fine-tuning the open-source LLM to achieve satisfactory results.
About expert
A AI Developer.
Grigorij applied their knowledge of LLM Fine-Tuning and Open-Source LLMs in Robotics on the following markets: Global.
Grigorij built products like LLM-Powered Robot using the variety of tools.
Other cases by Grigorij
TinderGPT: An AI Dating Assistant
TinderGPT is designed as an AI assistant to streamline the dating experience, sparing users from the time-consuming and often uncertain process of messaging on dating apps. This AI handles everything on behalf of the user—from initiating conversations with messages tailored to the profile in question, crafting engaging interactions, to arranging dates and providing the user with the match’s contact information.
Crafted a Web App for cosmetics recommendations employing ML and AI technologies.
As CTO at PrimerAI, I developed an intelligent platform for personalized cosmetics suggestions using ML and AI, including OpenCV, GPT-4, and K-means clustering for skin tone detection and categorization. I employed a holistic approach in developing and implementing this web app, which analyzed users' skin tones using computer vision and unsupervised ML algorithms. My contributions covered all aspects of the project, including designing and building the frontend and backend of the web app, as well as administering Linux servers. This ensured a seamless, end-to-end product experience that catered to our users' needs.
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Organized the company's participation in high-profile contests, resulting in Nimb becoming a winner of Smart City Expo worldwide Congress’s call for solution in the “Safe Cities” category and a finalist of Xprize Women’s Safety competition
Set up efficient client support
Enhancing Analytical Infrastructure and Reducing Fraudulent Activities
Led the shift of analytical infrastructure, enhancing data resources and decreasing update errors by 90%. Increased identifiable traffic sources from 60% to 95%. Played a crucial role in reducing fraudulent responses from 28% to 1-4% by strengthening moderation and creating a fraud analytics direction. Contributed to a project reducing the cost of applications from groups by 70% and reducing vacancies in central Moscow from 20% to 6%.
