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Sam Altman has been rehired at OpenAI, but there are concerns with the makeup of the new board and the future direction of the AI company. Some, including Scott Galloway, feel the nonprofit board and a for-profit company will never mix. Phillip Deng has a different perspective that I think is interesting to consider.
Lelapa AI hopes to build language models unique to Africa. The company aims to address the lack of AI tools that work for African languages and recognize African names and places, which currently excludes African people from economic opportunities. their goal is to reverse the brain drain by enticing African AI researchers to return to the continent and use their talents to produce homegrown African AI.
Starling-7B is an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). The model harnesses the power of our new GPT-4 labeled ranking dataset, Nectar, and our new reward training and policy tuning pipeline.
What is Qlora anyway? And how can it help your prompting
QLora stands for Quantization and Low-Rank Adapters. What does that even mean? It combines quantization and Low-Rank Adapters (LoRA) to reduce the memory requirements for fine-tuning massive language models with billions of parameters on a single GPU, making it more accessible to researchers and practitioners.
QLoRA is an extension of LoRA that introduces quantization, a process of mapping continuous infinite values to a smaller set of discrete finite values. This reduces the memory footprint of the model while retaining the necessary precision for training. QLoRA optimizes both computational and memory requirements during the training process, without sacrificing performance
QLoRA is a technique used in the fine-tuning of machine learning models, and its use is determined by how you configure and train your model. If you wish to use QLoRA, you would typically need to explicitly use it in your code when setting up and training your model.
There is no user-level switch to toggle on/off. It is built into the machinery of the LLM system and allows for a smaller footprint as far as memory usage. So it is more efficient when writing a response to your prompt. This is not something you would see or have any control over unless you were to create your bot the now old-fashioned way of using functions in ChatGPT or some other tool.