transformers

A SOTA quantization algorithm for high-accuracy low-bit LLM inference, seamlessly optimized for CPU/XPU/CUDA, with multi-datatype support and full compatibility with vLLM, SGLang, and Transformers.

Unified Training of Universal Time Series Forecasting Transformers

Implementation of SoundStorm, Efficient Parallel Audio Generation from Google Deepmind, in Pytorch

MLeap: Deploy ML Pipelines to Production

Over TypeScript tool to use custom transformers in the tsconfig.json

CodeTF: One-stop Transformer Library for State-of-the-art Code LLM

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

A curriculum for learning about foundation models, from scratch to the frontier

Interface for OuteTTS models.

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

Korean BERT pre-trained cased (KoBERT)

Implementation of the training framework proposed in Self-Rewarding Language Model, from MetaAI

Pytorch implementation of Transfusion, "Predict the Next Token and Diffuse Images with One Multi-Modal Model", from MetaAI

Fine-tune LLMs on your Mac with Apple Silicon. SFT, DPO, GRPO, Vision, TTS, STT, Embedding, and OCR fine-tuning — natively on MLX. Unsloth-compatible API.

🩹Editing large language models within 10 seconds⚡

Scalable and memory-optimized training of diffusion models

Generative Adversarial Transformers

An elegent pytorch implement of transformers

Curated visual catalog of 155+ vision-language model (VLM/MLLM) architectures: papers, diagrams, training recipes, datasets, and a release timeline for multimodal AI agents.

Jlama is a modern LLM inference engine for Java