Search Results for "transfer function model"
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Showing 390 open source projects for "transfer function model"
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Large Concept Model
Language modeling in a sentence representation space
Large Concept Model is a research codebase centered on concept-centric representation learning at scale, aiming to capture shared structure across many categories and modalities. It organizes training around concepts (rather than just raw labels), encouraging models to understand attributes, relations, and compositional structure that transfer across tasks. -
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Mooncake
Mooncake is the serving platform for Kimi
Mooncake is an open-source infrastructure platform designed to optimize large language model serving by focusing on efficient management and transfer of model data and KV cache. The platform was originally developed as part of the serving infrastructure for the Kimi large language model system. Its architecture centers on a high-performance transfer engine that provides unified data transfer across different storage and networking technologies. ... -
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VMZ (Video Model Zoo)
VMZ: Model Zoo for Video Modeling
...It also integrates Gradient Blending, an audio-visual modeling method that fuses modalities effectively (available in the Caffe2 implementation). Although VMZ is now archived and no longer actively maintained, it remains a valuable reference for understanding early large-scale video model training, transfer learning, and multimodal integration strategies that influenced modern architectures like SlowFast and X3D. -
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spacy-transformers
Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
spaCy supports a number of transfer and multi-task learning workflows that can often help improve your pipeline’s efficiency or accuracy. Transfer learning refers to techniques such as word vector tables and language model pretraining. These techniques can be used to import knowledge from raw text into your pipeline, so that your models are able to generalize better from your annotated examples. -
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Qwen3-Coder
Qwen3-Coder is the code version of Qwen3
...Qwen3-Coder supports an exceptionally long context window of 256,000 tokens, extendable to 1 million tokens using Yarn, enabling repository-scale code understanding and generation. It is capable of handling 358 programming languages, from common to niche, making it versatile for a wide range of development environments. The model integrates a specially designed function call format and supports popular platforms such as Qwen Code and CLINE for agentic coding workflows. -
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ControlSystems.jl
A Control Systems Toolbox for Julia
ControlSystems.jl is a Julia toolbox for control systems design and analysis, offering models in transfer-function and state-space representations, enabling construction of complex systems, simulation in time and frequency domains, and performance/stability evaluation. This toolbox works similar to that of other major computer-aided control systems design (CACSD) toolboxes. Systems can be created in either a transfer function or a state space representation. ... -
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Mixed-effects models in Julia
A Julia package for fitting (statistical) mixed-effects models
This package defines linear mixed models (LinearMixedModel) and generalized linear mixed models (GeneralizedLinearMixedModel). Users can use the abstraction for statistical model API to build, fit (fit/fit!), and query the fitted models. A mixed-effects model is a statistical model for a response variable as a function of one or more covariates. For a categorical covariate the coefficients associated with the levels of the covariate are sometimes called effects, as in "the effect of using Treatment 1 versus the placebo". ... -
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Nexa SDK
Nexa SDK is a comprehensive toolkit for supporting ONNX and GGML
Nexa SDK is a comprehensive toolkit for supporting ONNX and GGML models. It supports text generation, image generation, vision-language models (VLM), and speech-to-text (ASR), and text-to-speech (TTS) capabilities. Additionally, it offers an OpenAI-compatible API server with JSON schema mode for function calling and streaming support, and a user-friendly Streamlit UI. Users can run Nexa SDK in any device with Python environment, and GPU acceleration is supported, including CUDA, Metal, and... -
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Ziggy
Use your Laravel routes in JavaScript
Ziggy provides a JavaScript route() function that works like Laravel's, making it a breeze to use your named Laravel routes in JavaScript. Ziggy supports Laravel's route-model binding, and can even recognize custom route key names. If you pass route() a JavaScript object as a route parameter, Ziggy will use the registered route-model binding keys for that route to find the correct parameter value inside the object. -
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Bayesian Statistics
This repository holds slides and code for a full Bayesian statistics
This repository holds slides and code for a full Bayesian statistics graduate course. Bayesian statistics is an approach to inferential statistics based on Bayes' theorem, where available knowledge about parameters in a statistical model is updated with the information in observed data. The background knowledge is expressed as a prior distribution and combined with observational data in the form of a likelihood function to determine the posterior distribution. The posterior can also be used for making predictions about future events. Bayesian statistics is a departure from classical inferential statistics that prohibits probability statements about parameters and is based on asymptotically sampling infinite samples from a theoretical population and finding parameter values that maximize the likelihood function. -
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EduCDM
The Model Zoo of cognitive diagnosis models
The Model Zoo of Cognitive Diagnosis Models, including classic Item Response Ranking (IRT), Multidimensional Item Response Ranking (MIRT), Deterministic Input, Noisy "And" model(DINA), and advanced Fuzzy Cognitive Diagnosis Framework (FuzzyCDF), Neural Cognitive Diagnosis Model (NCDM), Item Response Ranking framework (IRR), Incremental Cognitive Diagnosis (ICD) and Knowledge-association baesd extension of NeuralCD (KaNCD). Cognitive diagnosis model (CDM) for intelligent educational systems... -
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Groq Desktop
Local Groq Desktop chat app with MCP support
Groq Desktop is a cross-platform (Windows / macOS / Linux) local desktop application that provides a graphical chat interface for interacting with Groq-hosted, function-call-capable models. It bundles a built-in MCP (Model Context Protocol) server enabling smart function calling, letting users chat with an AI, send images, or interact with richer multimodal inputs — all from a friendly desktop UI. The app is built with modern web technologies and packaged for native distribution, making it accessible even to non-developer users who just want to experiment with Groq models without writing code. ... -
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gpt-oss
gpt-oss-120b and gpt-oss-20b are two open-weight language models
gpt-oss is OpenAI’s open-weight family of large language models designed for powerful reasoning, agentic workflows, and versatile developer use cases. The series includes two main models: gpt-oss-120b, a 117-billion parameter model optimized for general-purpose, high-reasoning tasks that can run on a single H100 GPU, and gpt-oss-20b, a lighter 21-billion parameter model ideal for low-latency or specialized applications on smaller hardware. Both models use a native MXFP4 quantization for efficient memory use and support OpenAI’s Harmony response format, enabling transparent full chain-of-thought reasoning and advanced tool integrations such as function calling, browsing, and Python code execution. ... -
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Surrogates.jl
Surrogate modeling and optimization for scientific machine learning
A surrogate model is an approximation method that mimics the behavior of a computationally expensive simulation. In more mathematical terms: suppose we are attempting to optimize a function f(p), but each calculation of f is very expensive. It may be the case we need to solve a PDE for each point or use advanced numerical linear algebra machinery, which is usually costly. -
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MATLAB Deep Learning Model Hub
Discover pretrained models for deep learning in MATLAB
Discover pre-trained models for deep learning in MATLAB. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning. Inputs are RGB images, the output is the predicted label and score. -
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PHPMailer
The classic email sending library for PHP
PHPMailer – A full-featured email creation and transfer class for PHP. Support for UTF-8 content and 8bit, base64, binary, and quoted-printable encodings. SMTP authentication with LOGIN, PLAIN, CRAM-MD5, and XOAUTH2 mechanisms over SMTPS and SMTP+STARTTLS transports. Validates email addresses automatically. Many PHP developers need to send email from their code. The only PHP function that supports this directly is mail(). -
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PaddleX
PaddlePaddle End-to-End Development Toolkit
...Image classification and labeling is the most basic and simplest labeling task. Users only need to put pictures belonging to the same category in the same folder. When the model is trained, we need to divide the training set, the validation set and the test set. Therefore, we need to divide the above data. Using the paddlex command, the data set can be randomly divided into 70% training set, 20% validation set and 10% test set. If you use the PaddleX visualization client for model training, the data set division function is integrated in the client, and you do not need to use command division by yourself. -
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UMAP.jl
Uniform Manifold Approximation and Projection (UMAP) implementation
A pure Julia implementation of the Uniform Manifold Approximation and Projection dimension reduction algorithm. The umap function takes two arguments, X (a column-major matrix of shape (n_features, n_samples)), n_components (the number of dimensions in the output embedding), and various keyword arguments. -
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Chat UI
The open source codebase powering HuggingChat
Hugging Face Chat UI is an open-source web interface designed for interacting with large language models through a modern conversational interface. The project serves as the codebase behind HuggingChat and can be deployed locally or on cloud infrastructure to create customizable AI chat applications. Built with modern web technologies such as SvelteKit and backed by MongoDB for persistence, the interface provides a responsive environment for multi-turn conversations, file handling, and... -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. -
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MetaCLIP
ICLR2024 Spotlight: curation/training code, metadata, distribution
...It includes utilities to fine-tune vision-language embeddings, compute prompt or adapter updates, and benchmark across transfer and retention metrics. MetaCLIP is especially suited for real-world settings where a model must continuously incorporate new visual categories or domains over time. -
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Shelf
Web server middleware for Dart
Shelf makes it easy to create and compose web servers and parts of web servers. How? Expose a small set of simple types. Map server logic into a simple function: a single argument for the request, the response is the return value. Trivially mix and match synchronous and asynchronous processing. Flexibility to return a simple string or a byte stream with the same model. An adapter must handle all errors from the handler, including the handler returning a null response. It should print each error to the console if possible, then act as though the handler returned a 500 response. ... -
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Functions Framework for Node.js
FaaS (Function as a service) framework for writing portable Node.js
Functions Framework for Node.js is a lightweight runtime library that allows developers to write and run portable serverless functions locally or in cloud environments using standard Node.js. It provides a consistent execution model that mimics Google Cloud Functions, enabling developers to build, test, and debug functions on their local machine before deploying them to production. The framework supports multiple function signature types, including HTTP-based functions and event-driven functions, and automatically parses incoming request payloads into structured objects that can be easily consumed by application logic. ... -
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handson-ml2
Jupyter notebooks that walk you through the fundamentals of ML
This repository contains the Jupyter notebooks and code for the second edition of a popular hands-on machine learning book that teaches both classical ML and deep learning using modern tooling. The notebooks emphasize end-to-end workflows: data preparation, model selection, tuning, and reliable evaluation. Deep learning sections use the contemporary Keras/TensorFlow 2 ecosystem, highlighting clean APIs and eager execution to make experiments easier to reason about. Traditional ML topics remain central, with scikit-learn pipelines, feature engineering, and cross-validation patterns that transfer to real projects. ... -
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Keras Hub
Pretrained model hub for Keras 3
Keras Hub is a repository of pre-trained models for Keras 3, offering a collection of ready-to-use models for various machine-learning tasks. KerasHub is an extension of the core Keras API; KerasHub components are provided as Layer and Model implementations. If you are familiar with Keras, congratulations. You already understand most of KerasHub.
