Search Results for "cp/m"
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Showing 53 open source projects for "cp/m"
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1
Z80-μLM
Z80-μLM is a 2-bit quantized language model
...The repository provides a complete workflow where you train or fine-tune conversational models in Python, then export them into a format that can be executed on classic Z80 systems. A key deliverable is producing CP/M-compatible .COM binaries, enabling a genuinely vintage “chat with your computer” experience on real hardware or accurate emulators. The project sits at the intersection of machine learning and systems constraints, showing how model architecture, quantization, and inference code generation can be adapted to extreme memory and compute limits. ... -
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Machine Learning Systems
Introduction to Machine Learning Systems
Machine Learning Systems is an open educational repository that serves as the source and learning stack for the Machine Learning Systems textbook, a project focused on teaching how to engineer AI systems that work reliably in real-world environments. Rather than concentrating only on model training, the material emphasizes the broader discipline of AI engineering, covering efficiency, reliability, deployment, and evaluation across the full lifecycle of intelligent systems. The repository... -
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ML.NET
Open source and cross-platform machine learning framework for .NET
With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that... -
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
This curated list contains 900 awesome open-source projects with a total of 3.3M stars grouped into 34 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Kitten TTS
State-of-the-art TTS model under 25MB
KittenTTS is an open-source, ultra-lightweight, and high-quality text-to-speech model featuring just 15 million parameters and a binary size under 25 MB. It is designed for real-time CPU-based deployment across diverse platforms. Ultra-lightweight, model size less than 25MB. CPU-optimized, runs without GPU on any device. High-quality voices, several premium voice options available. Fast inference, optimized for real-time speech synthesis. -
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AutoKeras
AutoML library for deep learning
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras only support Python 3. If you followed previous steps to use virtualenv to install tensorflow, you can just activate the virtualenv. Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0. AutoKeras supports several tasks with extremely simple interface. -
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GPTel
A no-frills ChatGPT client for Emacs
...Supports conversations (not just one-off queries) and multiple independent sessions. You can go back and edit your previous prompts, or even ChatGPT’s previous responses when continuing a conversation. These will be fed back to ChatGPT. Run M-x gptel to start or switch to the ChatGPT buffer. It will ask you for the key if you skipped the previous step. Run it with a prefix-arg to start a new session. In the gptel buffer, send your prompt with M-x gptel-send, bound to C-c RET. Set chat parameters (GPT model, directives etc) for the session by calling gptel-send with a prefix argument. -
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Step1X-3D
High-Fidelity and Controllable Generation of Textured 3D Assets
Step1X-3D is an open-source framework for generating high-fidelity textured 3D assets from scratch — both their geometry and surface textures — using modern generative AI techniques. It combines a hybrid architecture: a geometry generation stage using a VAE-DiT model to output a watertight 3D representation (e.g. TSDF surface), and a texture synthesis stage that conditions on geometry and optionally reference input (or prompts) to produce view-consistent textures using a diffusion-based... -
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MLX-Audio
A text-to-speech, speech-to-text and speech-to-speech library
MLX-Audio is a speech library built on Apple’s MLX framework and optimized for Apple Silicon machines (M-series Macs). It focuses on text-to-speech and speech-to-speech workflows, with APIs and a command-line interface that make it easy to generate high-quality audio from text. Because it uses MLX and targets Apple Silicon, inference is fast and can take advantage of hardware acceleration and quantization for efficient on-device performance. -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Porcupine
On-device wake word detection powered by deep learning
...It enables building always-listening voice-enabled applications. It is using deep neural networks trained in real-world environments. Compact and computationally-efficient. It is perfect for IoT. Cross-platform. Arm Cortex-M, STM32, PSoC, Arduino, and i.MX RT. Raspberry Pi, NVIDIA Jetson Nano, and BeagleBone. Android and iOS. Chrome, Safari, Firefox, and Edge. Linux (x86_64), macOS (x86_64, arm64), and Windows (x86_64). Scalable. It can detect multiple always-listening voice commands with no added runtime footprint. Self-service. Developers can train custom wake word models using Picovoice Console. ... -
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TurboFieldfare
Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook
TurboFieldfare is a custom Swift and Metal runtime for running the instruction-tuned Gemma 4 26B-A4B model on Apple Silicon Macs with limited memory. Instead of loading the entire model, it keeps the shared core and KV cache in RAM while streaming only the routed experts required for each token from SSD. This approach reduces active memory use to roughly 2 GB while the installed model occupies about 14.3 GB of storage. The project includes a native Mac application, command-line tools, a... -
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TreeQuest
A Tree Search Library with Flexible API for LLM Inference-Time Scaling
TreeQuest, developed by SakanaAI, is a versatile Python library implementing adaptive tree search algorithms—such as AB‑MCTS—for enhancing inference-time performance of large language models (LLMs). It allows developers to define custom state-generation and scoring functions (e.g., via LLMs), and then efficiently explores possible answer trees during runtime. With support for multi-LLM collaboration, checkpointing, and mixed policies, TreeQuest enables smarter, trial‑and‑error question... -
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Regex
Generate matching and non matching strings based on regex patterns
...It is highly doubtful anyone would require a string of infinite length, thus I've artificially limited repetitions in such patterns to 100 symbols when generating random values. Use a{n,m} if you require some specific number of repetitions. It is suggested to avoid using such infinite patterns to generate data based on regex. -
14
uzu
A high-performance inference engine for AI models
...Written primarily in Rust and leveraging Apple’s Metal framework, the project focuses on maximizing performance when executing large language models and other AI workloads on devices such as Mac computers with M-series chips. The engine implements a hybrid architecture in which model layers can be executed either as custom GPU kernels or through Apple’s MPSGraph API, allowing it to balance performance and compatibility depending on the workload. By utilizing Apple’s unified memory architecture, uzu reduces memory copying overhead and improves inference throughput for local AI workloads. ... -
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MLX Engine
LM Studio Apple MLX engine
...Built on top of the mlx-lm and mlx-vlm ecosystems, the engine provides a unified architecture capable of supporting both text-only and multimodal models. Its design focuses on high-performance on-device inference, leveraging Apple’s MLX stack to accelerate computation on M-series chips. The project introduces modular VisionAddOn components that allow image embeddings to be integrated seamlessly into language model workflows. It is bundled with newer versions of LM Studio but can also be used independently for experimentation and development. Overall, mlx-engine serves as a specialized high-efficiency runtime for local AI workloads on macOS systems. -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
...AutoGluon is modularized into sub-modules specialized for tabular, text, or image data. You can reduce the number of dependencies required by solely installing a specific sub-module via: python3 -m pip install <submodule>. -
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DxTonia
A tool for detecting the presence of leg dystonia from videos.
An open-source diagnostic tool for detecting the presence of leg dystonia from videos. The software provides an automated solution for detecting leg dystonia in children with cerebral palsy (CP), by using artificial intelligence. The software has been verified on two datasets and achieved higher accuracy than routine clinical care. Stay tuned for the improved version. Note: Currently only works on Windows PC with Nvidia GPU. Soon, mac and Linux CPU only version will be released. The requirment files are in old-version_scripts folder. ... -
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Revenant OS
Sovereign, Local-First Agentic AI Linux OS for Toughbook CF-52.
...Key Features: - 100% Offline AI: Embedded llama-server with Qwen2.5-Coder-1.5B (no cloud or API keys needed). - Full-Duplex Voice: Whisper STT (whisper.cpp) + fast Piper neural TTS. - Single-Window Voice Coordinator: Global Super+M hotkey with Readline pre-filling. - Toughbook Optimizations: Native CF-52 hotkeys, battery telemetry, and ALSA auto-unmute. - Dual Desktops: Dark XFCE desktop + lightweight i3 Tiling Window Manager. - Hardened & Lightweight: Default UFW firewall, ~350MB RAM reclaimed from legacy proxies. Releases & Documentation: https://github.com/Fixitdaz/revenant-os -
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GeneralAI
Large-scale Self-supervised Pre-training Across Tasks, Languages, etc.
Fundamental research to develop new architectures for foundation models and AI, focusing on modeling generality and capability, as well as training stability and efficiency. -
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Karlo
Text-conditional image generation model based on OpenAI's unCLIP
Karlo is a text-conditional image generation model based on OpenAI's unCLIP architecture with the improvement over the standard super-resolution model from 64px to 256px, recovering high-frequency details only in the small number of denoising steps. We train all components from scratch on 115M image-text pairs including COYO-100M, CC3M, and CC12M. In the case of Prior and Decoder, we use ViT-L/14 provided by OpenAI’s CLIP repository. Unlike the original implementation of unCLIP, we... -
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MX Terminal
A chat style app for the M32, X32, M-Air, X-Air digital consoles
MX Terminal is a simple chat style app that can control the Midas and Behringer digital consoles including the M32, X32, M-Air and X-Air. Built on the popular Live Toolbox OSC engine, the text based interface provides the user full OSC,tidbit and the new English commands sets. Responses from the console can be returned in real world values (db, hz, etc.) instead of OSC values.And text to speech (TTS) is available for these responses for the visually impaired. -
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...This software is under GNU General Public License version 3.0 (GPLv3) This code is a demo of method described by the following publication: Zhu, Y., Ting, K.M., Carman, M. and Angelova, M., 2021, April. CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. Pattern Recognition. https://doi.org/10.1016/j.patcog.2021.107977 The preprint version can be obtained at: https://arxiv.org/abs/1810.02897
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uTensor
TinyML AI inference library
...This approach allows developers to build machine learning models using standard frameworks and then deploy them to devices with extremely limited memory and processing power. The runtime library is intentionally lightweight and optimized for platforms such as Arm Cortex-M microcontrollers, making it suitable for real-time edge applications. -
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MetaErg
Metagenome Annotation Pipeline
MetaErg is a stand-alone and fully automated metagenome and metaproteome annotation pipeline published at: https://www.frontiersin.org/articles/10.3389/fgene.2019.00999/full. If you are using this pipeline for your work, please cite: Dong X and Strous M (2019) An Integrated Pipeline for Annotation and Visualization of Metagenomic Contigs. Front. Genet. 10:999. doi: 10.3389/fgene.2019.00999 The instructions on configuring and running the MetaErg pipeline is available at GitHub repository: https://github.com/xiaoli-dong/metaerg -
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CUDA-JMI
Tool for feature selection using the JMI metric and multiple GPUs
CUDA-JMI is a parallel tool to accelerate the feature selection process using Joint Mutual Information as metric. This tool receives as input a file with ARFF, CVS or LIBSVM extensions that contais the values of m individuals and n features and returns a file with those features that provide more non-rendundant information.
