Contextual Learning and IndexingModern generative Large Language Models (LLMs) demonstrate contextual learning capabilities, allowing them to perform new tasks without weight updates. By providing a few examples within the input prompt, the model can infer the desired pattern and generate appropriat...
The StarCoder2 suite comprises architecture variants scaled at 3 billion, 7 billion, and 15 billion parameters. Training utilized a corpus ranging between 3.3 and 4.3 trillion code tokens sourced from the Stack v2 dataset, encompassing support for over 600 distinct programming languages. Architectur...
Background The trajectory of artificial intelligence has undergone remarkable transformations since the formal inception of AI research in the 1950s. The emergence of deep learning algorithms in recent years has catalyzed unprecedented advancements across multiple domains. Large language models, cha...
Google DeepMind's Gemma language models are available with open weights on Hugging Face. The family includes 2B and 7B parameter variants, offered in both pre-trained and instruction-tuned versions. These models are supported on the Hugging Face platform and can be deployed and fine-tuned on service...
Local Model Deployment First, acquire the ChatGLM3-6B model. It can be downloaded from its GitHub repository or the Hugging Face Hub. After downloading, set up a Python virtual environment and install the necessary dependencies. LORA Fine-Tuning Dataset Preparation Construct a dataset in a conversat...