Apache TVM is a compiler stack for deep learning systems. It is designed to close the gap between the productivity-focused deep learning frameworks, and the performance- and efficiency-focused hardware backends. TVM works with deep learning frameworks to provide end to end compilation to different backends

HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. HDBSCAN is ideal for exploratory data analysis; it's a fast and robust algorithm that you can trust to return meaningful clusters (if there are any).

This package implements the Hopcroft-Karp algorithm, producing a maximum cardinality matching from a bipartite graph.

SpaCy is a library for advanced Natural Language Processing in Python and Cython. It comes with pretrained pipelines and currently supports tokenization and training for 70+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management.

This package provides a Python library for probabilistic modeling and inference.

This package provides simple access speech to text for using in Linux without being tied to a desktop environment, using the vosk-api. The user configuration lets you manipulate text using Python string operations. It has zero overhead, as this relies on manual activation and there are no background processes. Dictation is accessed manually with nerd-dictation begin and nerd-dictation end commands.

This package provides a Python library to easily read single characters and key strokes.

This package provides a functional take on deep learning, compatible with your favorite libraries.

PyNNDescent provides a Python implementation of Nearest Neighbor Descent for k-neighbor-graph construction and approximate nearest neighbor search.

LIBSVM is a machine learning library for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

apricot implements submodular optimization for the purpose of summarizing massive data sets into minimally redundant subsets that are still representative of the original data. These subsets are useful for both visualizing the modalities in the data and for training accurate machine learning models with just a fraction of the examples and compute.

frugally-deep is a header-only C++ library for inference of Keras machine learning models on a single CPU core.

This package is a stand-alone implementation of several NumPy dtype extensions used in machine learning libraries, including:

• bfloat16: an alternative to the standard float16 format

• float8_*: several experimental 8-bit floating point representations including:

  • float8_e4m3b11fnuz

  • float8_e4m3fn

  • float8_e4m3fnuz

  • float8_e5m2

  • float8_e5m2fnuz

• int4 and uint4: low precision integer types.

The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.

This package provides simple access speech to text for using in Linux without being tied to a desktop environment, using the vosk-api. The user configuration lets you manipulate text using Python string operations. It has zero overhead, as this relies on manual activation and there are no background processes. Dictation is accessed manually with nerd-dictation begin and nerd-dictation end commands.

This package provides a GStreamer plugin that wraps Kaldi's SingleUtteranceNnet2Decoder. It requires iVector-adapted DNN acoustic models. The iVectors are adapted to the current audio stream automatically.

KoboldCpp is an easy-to-use AI text-generation software for GGML and GGUF models, builds off llama.cpp and adds many additional features:

• Runs on CPU or GPU, supports full or partial offloaded

• LLM text generation (Supports all GGML and GGUF models, backwards compatibility with ALL past models)

• Image Generation (Stable Diffusion 1.5, SDXL, SD3, Flux)

• Speech-To-Text (Voice Recognition) via Whisper

• Text-To-Speech (Voice Generation) via OuteTTS, Kokoro, Parler and Dia

• Provides many compatible APIs endpoints for many popular webservices (KoboldCppApi OpenAiApi OllamaApi A1111ForgeApi ComfyUiApi WhisperTranscribeApi XttsApi OpenAiSpeechApi)

• Bundled KoboldAI Lite UI with editing tools, save formats, memory, world info, author's note, characters, scenarios

• Includes multiple modes (chat, adventure, instruct, storywriter) and UI Themes (aesthetic roleplay, classic writer, corporate assistant messsenger)

• Supports loading Tavern Character Cards, importing many different data formats from various sites, reading or exporting JSON savefiles and persistent stories

• Many other features including new samplers, regex support websearch, RAG via TextDB, image recognition/vision and more

All up-to-date GGUF models are supported, and KoboldCpp also includes backward compatibility for older versions/legacy GGML models.

This framework provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa and achieve state-of-the-art performance in various tasks. Text is embedded in vector space such that similar text are closer and can efficiently be found using cosine similarity.

This package provides easy access to pretrained models for more than 100 languages, fine-tuned for various use-cases.

Further, this framework allows an easy fine-tuning of custom embeddings models, to achieve maximal performance on your specific task.

This package provides simple access speech to text for using in Linux without being tied to a desktop environment, using the vosk-api. The user configuration lets you manipulate text using Python string operations. It has zero overhead, as this relies on manual activation and there are no background processes. Dictation is accessed manually with nerd-dictation begin and nerd-dictation end commands.

Uniform Manifold Approximation and Projection is a dimension reduction technique that can be used for visualization similarly to t-SNE, but also for general non-linear dimension reduction.

TensorPipe provides a tensor-aware channel to transfer rich objects from one process to another while using the fastest transport for the tensors contained therein.

This package provides the main server that Magic-Wormhole clients connect to. The server performs store-and-forward delivery for small key-exchange and control messages. Bulk data is sent over a direct TCP connection, or through a transit-relay.

This package provides the Magic-Wormhole Transit Relay server, which helps clients establish bulk-data transit connections even when both are behind NAT boxes. Each side makes a TCP connection to this server and presents a handshake. Two connections with identical handshakes are glued together, allowing them to pretend they have a direct connection.

Magic-Wormhole is a library and a command-line tool named wormhole, which makes it possible to securely transfer arbitrary-sized files and directories (or short pieces of text) from one computer to another. The two endpoints are identified by using identical "wormhole codes": in general, the sending machine generates and displays the code, which must then be typed into the receiving machine.

The codes are short and human-pronounceable, using a phonetically-distinct wordlist. The receiving side offers tab-completion on the codewords, so usually only a few characters must be typed. Wormhole codes are single-use and do not need to be memorized.