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DeepNewz, mobile.
People-sourced. AI-powered. Unbiased News.
Recent advancements in machine learning models have shown promising results in quantization methods for 2-bit and 1-bit LLMs, leading to significant reductions in model sizes and memory footprints. Companies like Databricks, Mistral, and Apple have introduced new models with improved performance and efficiency. These developments mark a significant shift in the landscape of large language models, with potential implications for training and inference speeds.
Potentially the biggest paradigm shift in LLMs Two independent studies managed to pre-train 1.58-bit LLMs that match the performance of FP16 models. Need to see how it scales (~30B), but super curious about 1.58-bit Mamba and MoE models. https://t.co/56EepNqIgP https://t.co/xybyVHBgTi https://t.co/QpCrlu4oJu
so last month msft published a paper showing a 1 bit parameter LLM with minimal performance loss. someone on huggingface just replicated the results today. this is at least a 10x reduction memory footprint and opens up a path for even more gains in training / inference speeds https://t.co/ApHeGZDrFA
๐ #ElonMusk's AI leaps forward with Grok-1.5, boasting superior math skills. ๐ #Databricks debuts its model, setting a new benchmark. ๐ #AI21Labs introduces Jamba, merging Mamba with Transformer architecture. Read more: https://t.co/rOSYi59xTY https://t.co/Zm2upLfVcr
