Transformer Models
Showing 25 of 25 projects
Official inference framework for 1-bit LLMs, enabling fast and lossless CPU/GPU inference with significant speed and energy efficiency gains.
Industrial-strength Natural Language Processing library for Python, featuring pretrained pipelines for 70+ languages and production-ready training.
A Python framework for computing and training state-of-the-art text embeddings, rerankers, and sparse encoders.
A comprehensive library for post-training foundation models using reinforcement learning and fine-tuning techniques.
A PyTorch library providing 12+ semantic segmentation model architectures with 800+ pretrained convolutional and transformer-based encoders.
A Python library offering scalable and user-friendly implementations of state-of-the-art neural forecasting models.
A state-of-the-art Natural Language Processing library built on Apache Spark, offering 100,000+ pretrained models and pipelines in 200+ languages.
A Rust-native port of Hugging Face Transformers providing ready-to-use NLP pipelines and transformer models like BERT, GPT2, and T5.
A modular toolkit for machine learning, natural language processing, and text generation with TensorFlow and PyTorch versions.
A high-performance, scalable LLM library and reference implementation written in pure Python/JAX for training on TPUs and GPUs.
A JAX research toolkit for building, editing, and visualizing neural networks as legible, functional pytree data structures.
State-of-the-art pre-trained transformer language models for protein sequences, enabling tasks like structure prediction and function annotation.
A curated list of recent research papers and resources on Vision and Language Pre-trained Models (VL-PTMs).
A lightweight Ruby playground with clean, readable implementations of core AI algorithms for learning and experimentation.
Run ONNX transformer pipelines (like Hugging Face) natively in Go for inference and fine-tuning, with support for CPU, GPU, and TPU.
A JAX-based framework for streamlined training, fine-tuning, and high-performance serving of large language and multimodal models.
A curated list of open-source neural machine translation implementations across various deep learning frameworks.
A collection of genomic language models for predicting variant effects and evolutionary constraints from DNA sequences.
A pure Go package for running inference with pre-trained Transformer models from Hugging Face, enabling NLP tasks without external languages.
Scripts and tools to recreate the ELI5 dataset for long-form question answering research.
A transformer-based model for predicting drug-target interactions using substructural pattern mining and augmented transformer encoders.
A collection of pre-trained BERT, DistilBERT, ELECTRA, GPT-2, and ConvBERT models for multiple languages, including German, Italian, Turkish, and historic texts.
A Swift library for accelerated tensor operations and dynamic neural networks with automatic differentiation, supporting all Apple platforms and Linux.
A production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
Efficient inference implementation for Transformer models on edge devices, originally focused on OpenAI's Whisper speech recognition.
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