Transformer Models
Showing 30 of 30 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 production-ready deep learning framework for Go that enables training and deploying neural networks as single binaries with a PyTorch-like API.
A Swift library for accelerated tensor operations and dynamic neural networks with automatic differentiation, supporting all Apple platforms and Linux.
Efficient inference implementation for Transformer models on edge devices, originally focused on OpenAI's Whisper speech recognition.
A prompt injection scanner for Claude Code hooks that detects attacks, leaked secrets, and data exfiltration using ML models.
A repository for planning and training German transformer language models from scratch.
German language versions of GPT-2, trained on the CC-100 corpus and initialized from English GPT-2 weights.
A framework for evaluating German transformer language models using syntactic agreement tests.
A dynamic inference wrapper for Transformer language models that enables per-token layer skipping to reduce computational FLOPs.
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