All of my long-form thoughts on programming, tech, design, and more, collected in chronological order.
Fine-tuning large language models (LLMs) can be resource-intensive, requiring immense computational power. LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation) offer efficient alternatives for training these models while using fewer resources.
I recently embarked on an exciting research journey to explore the vulnerabilities of large language models (LLMs) like ChatGPT, Anthropic Gemini, and similar models. My goal was to see how hackers could exploit them through prompt injection attacks.
In the ever-evolving domain of Natural Language Processing (NLP), supervised fine-tuning has emerged as a game-changing technique for adapting pre-trained Large Language Models (LLMs) to specific tasks. While pre-trained LLMs like those in the GPT family have made significant strides in language comprehension and generation, they often lack optimization for particular applications.
LLama is undoubtably one of the best open-source LLM out there by Meta. If you have ever wandered how it works, how these LLMs are able to produce human like text and talk like humans. Well, you are not alone.
Recently moved from an Intel based processor to an M1 apple silicon Mac and had a hard time setting up my development environments and tools, especially for my machine learning projects, I was particularly exited to use the new Apple Silicon ARM64 architecture and benefit from the GPU acceleration it offers for my ML tasks.
Hey there, i'll take it you are here because you have had a hard time installing Mongodb on your macOS yes? Well i had the same issue and, below i will share the reason why and how you can fix this issue.