Search Results for "raspberry-gpio-python" - Page 3

BGE-M3 is an advanced text embedding model developed by BAAI that excels in multi-functionality, multi-linguality, and multi-granularity. It supports dense retrieval, sparse retrieval (lexical weighting), and multi-vector retrieval (ColBERT-style), making it ideal for hybrid systems in retrieval-augmented generation (RAG). The model handles over 100 languages and supports long-text inputs up to 8192 tokens, offering flexibility across short queries and full documents. BGE-M3 was trained...

Llama-2-7B is a foundational large language model developed by Meta as part of the Llama 2 family, designed for general-purpose text generation tasks. It is a 7 billion parameter auto-regressive transformer trained on 2 trillion tokens from publicly available sources, using an optimized architecture without Grouped-Query Attention (GQA). This model is the pretrained version, intended for research and commercial use in English, and can be adapted for downstream applications such as...

The voice-activity-detection model by pyannote is a neural pipeline for detecting when speech occurs in audio recordings. Built on pyannote.audio 2.1, it identifies segments of active speech within any audio file, making it valuable for preprocessing tasks like transcription, diarization, or voice-controlled systems. The model was trained using datasets such as AMI, DIHARD, and VoxConverse, and it requires users to authenticate via Hugging Face for access. To use the model, users must accept...

... probabilistic forecasting by autoregressively sampling multiple future trajectories. The model is capable of generating full predictive distributions, making it well-suited for uncertainty-aware forecasting. It is compatible with the Chronos Python package and integrates easily into forecasting pipelines using PyTorch. Chronos models are open-source under Apache 2.0 and have been demonstrated to perform competitively in forecasting benchmarks.

ERNIE-4.5-VL-28B-A3B-Paddle is a multimodal MoE chat model designed for complex image-text tasks, featuring 28 billion total parameters with 3 billion activated per token. Built on PaddlePaddle, it excels in tasks like visual question answering, description generation, and multimodal reasoning. It employs a heterogeneous Mixture-of-Experts architecture that supports both thinking and non-thinking inference modes. The model benefits from advanced pretraining and posttraining strategies,...