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PatchTST, a time series forecasting model based on Transformers, was added to Hugging Face with documentation showing how to train and apply it to electricity data using transfer learning. The model divides time series into patches of configurable length (the example uses patch_length=16 with context_length=512) and reduces computational complexity quadratically compared to processing full sequences. Users can now train PatchTST directly on datasets, perform zero-shot forecasting on new domains, and fine-tune pretrained models using the Hugging Face Trainer API.
Hugging Face Text Generation Inference became generally available on AWS Inferentia2 through Amazon SageMaker for deploying large language models in production. The solution supports popular models like Llama and Mistral, with pre-compiled configurations cached for batch size 2-4 and sequence length 2048 to avoid the 45-minute compilation process. Customers can now deploy LLMs on Inferentia2 as a cost-effective alternative to GPUs, with deployment taking 10-15 minutes on ml.inf2.8xlarge instances.
Researchers released Constitutional AI tools and datasets enabling open-source language models to self-align by critiquing their own outputs against user-defined principles. The team published the llm-swarm tool for generating synthetic training data at scale on GPU clusters, along with aligned Mistral 7B models and datasets based on both Anthropic's and a Grok-inspired constitution. This allows practitioners to customize model behavior without collecting expensive human feedback by having models identify and revise responses that violate constitutional principles.
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