A principled instruction benchmark on formulating effective queries and prompts for large language models (LLMs). Our paper: https://arxiv.org/abs/2312.16171

utilities for decoding deep representations (like sentence embeddings) back to text

A playbook for systematically maximizing the performance of deep learning models.

LLM training in simple, raw C/CUDA

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Flax is a neural network library for JAX that is designed for flexibility.

The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.

Micro Llama is a small Llama based model with 300M parameters trained from scratch with $500 budget

Code examples and resources for DBRX, a large language model developed by Databricks

Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch

1.58-bit LLaMa model

Toolkit for attaching, training, saving and loading of new heads for transformer models

Streamlit — A faster way to build and share data apps.

Build Conversational AI in minutes ⚡️

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

The Triton Inference Server provides an optimized cloud and edge inferencing solution.

Streamlined interface for generating images with AI in Krita. Inpaint and outpaint with optional text prompt, no tweaking required.

The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.

Open-Sora: Democratizing Efficient Video Production for All

MLX: An array framework for Apple silicon

Diffusion Bee is the easiest way to run Stable Diffusion locally on your M1 Mac. Comes with a one-click installer. No dependencies or technical knowledge needed.

Search photos on Unsplash using natural language

Finetune Llama 3, Mistral, Phi & Gemma LLMs 2-5x faster with 80% less memory

CLI to manage your datacontract.yaml files

Foundational model for human-like, expressive TTS

[ACL 2024] Official PyTorch code for extracting features and training downstream models with emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image