Search Results for "inference"

...The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples. whisper.cpp supports integer quantization of the Whisper ggml models. Quantized models require less memory and disk space and depending on the hardware can be processed more efficiently.

Genv is an open-source environment and cluster management system for GPUs. Genv lets you easily control, configure, monitor and enforce the GPU resources that you are using in a GPU machine or cluster. It is intended to ease up the process of GPU allocation for data scientists without code changes.

...It is designed to showcase the emerging field of RF-based sensing, where machine learning models interpret wireless channel data to reconstruct human movement and posture. The repository includes components for data processing, model inference, and real-time visualization, making it suitable for research and experimental deployments. Its architecture emphasizes performance and reproducibility, allowing developers to explore non-visual motion capture systems using accessible hardware. Overall, WiFi DensePose functions as an advanced research-grade toolkit for WiFi-based human sensing and pose estimation.

Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. Deep Learning Models (CV, LLM, and Generative) are an amazing technology that will power many of future ML use cases. A large set of these technologies are being deployed into businesses (the real world) in what we consider a production setting.

MiniSearch is a minimalist web search application with a built-in AI assistant that runs largely inside the browser for privacy-focused information retrieval. The project combines metasearch capabilities with local or remote language model inference to provide conversational answers alongside traditional search results. It is designed to be lightweight, easy to deploy with Docker, and configurable for both personal and hosted use cases. The platform supports browser-level integration so users can set it as their default search engine for quick access. Its architecture emphasizes privacy by avoiding tracking and minimizing data collection while still enabling advanced AI features. ...

The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which at the cost of RAM usage. So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. OnnxStream is based on the idea of decoupling the inference engine from the component responsible for providing the model weights, which is a class derived from WeightsProvider. ...

...A pre-trained English model is available for use and can be downloaded following the instructions in the usage docs. If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the DeepSpeech releases page.

Albedo is an open-source recommender system aimed at helping developers discover GitHub repositories by learning from activity signals. It treats repositories and developers as a graph of interactions and applies large-scale matrix factorization to model affinities, with Apache Spark providing the distributed data processing. The project focuses on implicit feedback—stars, watches, and other engagement metrics—so it can build useful recommendations without explicit ratings. A reproducible...

core.typed is an optional static type system for Clojure, providing gradual typing features like type declarations, checking, and inference. It supports modules for development-time checking and runtime typing, though note that it's deprecated as of Clojure 1.11 in favor of Typed Clojure. core.typed is separated into modules. You'll want the full type checker at development time, and the runtime dependencies in production. Then, lein repl will automatically activate the :dev profile. ...

Mandarax is a pure Java implementation of a rule engine. It supports mutiples types of facts and rules based on reflection, databases, EJB etc, supporting XML standards (RuleML 0.8). It provides a J2EE compliant inference engine using backward chaining.

Self-aware dynamic, reflective, and declarative semantic space representation, transformation, and exploration system.

SIES is a forward-chaining rule engine server. Clients connect to update a global knowledge-base and/or can register to be notified when specified elements are inferred. Local access for a client's own inferences is also possible.

The Prolog Expert System Shell (PESS) is a software that generates ES using basically two components: Knowledge Base, used by the ES to guide its decisions making, and Inference Machine, cable of collection the rules and generating new facts.