#Transformer models

Discover unified Python bindings for Transformer models implemented with GGML in C/C++. Compatible with models like GPT-2 and LLaMA, this package supports GPU layers and integrates with Hugging Face and LangChain. Available through PyPI for easy installation, it also features experimental attributes like GPTQ and streaming, complemented by extensive documentation.

SAGE offers cutting-edge spelling correction with sophisticated Transformer models for multiple languages. It simulates human error patterns through statistical and rule-based spelling corruption methods. The tool supports model testing and evaluation on benchmark datasets, establishing SAGE as a versatile resource for improving text accuracy. Recent advancements include a paper acceptance at EACL 2024, highlighting its effective methodologies and ongoing advancements in spelling correction technology.

Marlin, an FP16xINT4 optimized kernel, accelerates LLM inference with batch sizes of 16-32 tokens using advanced GPU techniques. It outperforms comparable kernels under various GPU conditions and is easily integrated with CUDA and torch. Key features include asynchronous global weight management and efficient resource allocation.

Scenic offers a robust framework for creating attention-based computer vision models, supporting tasks like classification and segmentation across multiple modalities. Utilizing JAX and Flax, it simplifies large-scale training through efficient pipelines and established baselines, ideal for research. Explore projects with state-of-the-art models like ViViT. Scenic provides adaptable solutions for both newcomers and experts, facilitating easy integration into existing workflows.

CTranslate2 facilitates efficient Transformer model inference with techniques like weight quantization and layer fusion, optimizing memory usage on both CPU and GPU. Supporting encoder-decoder, decoder-only, and encoder-only model types, it integrates seamlessly with multiple frameworks. Features include fast execution, reduced precision support, and minimal storage needs. The library's capabilities, such as automatic CPU selection, parallel processing, and ease of use in Python and C++, make it a reliable option for production use, distinctly outperforming general-purpose deep learning frameworks.

Delve into implementations of diverse transformer models like seq2seq, encoder-only, and decoder-only architectures for AI and deep learning enthusiasts. This open-source inspired repository offers insights into transformer advancements, attention mechanisms, and position embeddings. It serves as a valuable reference for programming skill enhancement and AI model development. Note: Intended for self-study, not formal educational assignments.