{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/4"}],"enrichment":{"capability":"Curated Transformers provides PyTorch implementations of state-of-the-art transformer models (BERT, Llama, Falcon, etc.) built from reusable components, with support for loading models from Hugging Face Hub and generation tasks.","skillfed_tags":["transformer-models","llm-inference","quantization"],"use_cases":["Load and run inference on large language models (Llama, Falcon) from Hugging Face Hub for text generation tasks.","Build encoder-only models (BERT, RoBERTa) for classification, token tagging, or semantic search tasks.","Quantize transformer models to 4-bit or 8-bit precision to reduce memory and improve inference speed on resource-constrained hardware.","Compose custom transformer architectures by mixing and matching reusable encoder/decoder components.","Integrate transformer inference into spaCy NLP pipelines via spacy-curated-transformers.","Study transformer internals through well-documented, type-annotated building blocks for educational purposes."],"what_it_does":"Curated Transformers is a PyTorch library that packages state-of-the-art transformer models as composable building blocks. It supports both encoder-only architectures (BERT, RoBERTa, XLM-RoBERTa) and decoder-only models (Llama, Falcon, GPT-NeoX, MPT), with generation wrappers for text generation tasks. Models load directly from Hugging Face Hub, and the library includes support for 4-bit and 8-bit quantization via bitsandbytes.\n\nThe library is designed around reusable components\u2014a single bugfix or feature (like 4-bit inference or PyTorch meta-device support) benefits all models at once. It emphasizes type safety with full type annotations on public APIs, making it suitable for both production use and education. Explosion AI uses it in production and it serves as the default transformer backend in spaCy 3.7.","worth_installing":"Yes, with conditions. Install if you need PyTorch transformer inference with clean, composable components and don't mind dormant maintenance (last release 849 days ago). The library is production-tested by Explosion AI and integrated into spaCy 3.7, but the long release gap means you should verify compatibility with your PyTorch and tokenizer versions before relying on it for new projects. No known security vulnerabilities."},"id":"curated-transformers","links":{"html":"https://skillfed.io/packages/curated-transformers","md":"https://skillfed.io/packages/curated-transformers.md","pypi":"https://pypi.org/project/curated-transformers/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2024-04-17","license_spdx":null,"license_treatment":"permissive","name":"curated-transformers","python_support":"supports_current","summary":"A PyTorch library of transformer models and components"},"popularity":{"monthly_downloads":924189,"position":4713,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"2.0.1"}