Search Results for "transfer function model"
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Showing 339 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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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 Although VMZ is now archived and no longer actively maintained, it remains a valuable reference for understanding early large-scale video model training, transfer learning, and multimodal integration strategies that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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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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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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Cactus Needle
26m function call model that runs on incredibly small devices
Needle is an experimental 26-million-parameter function-calling model designed to run on extremely small devices such as phones, watches, glasses, and low-power personal AI hardware. It is based on a Simple Attention Network architecture and was distilled from a much larger model to focus on fast, compact tool-use behavior. The project provides open weights, training details, dataset generation resources, and a playground for testing the model with custom tools. ... -
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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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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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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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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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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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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used across DeepMind. It preserves Sonnet’s module-based programming model for state management while retaining access to JAX’s function transformations. ... -
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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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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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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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MuseBot
Supports Telegram, Discord, Slack, Lark, compatible with LLMs
MuseBot is a multi-platform AI chatbot designed to connect popular messaging apps with large language model APIs. It supports Telegram, Discord, Slack, Lark, DingTalk, WeCom, QQ, WeChat, and a Web API for custom integrations. The bot can generate AI responses, stream output, understand images, process voice input, and use function-calling workflows. It supports model providers such as OpenAI, Gemini, DeepSeek, OpenRouter, Doubao, Qwen, and other compatible services. ... -
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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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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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gemini-web2api
Convert Google Gemini web into OpenAI-compatible API
...It is designed to let OpenAI-style clients connect to Gemini-like models through routes such as chat completions, models, responses, and native Gemini-compatible endpoints. The project can run as a simple local server and uses a mostly single-file design with an optional dependency for streaming. It supports model aliases for Flash, Thinking, Pro-style routing, Auto, and Lite variants. The tool also includes optional API keys, function calling, SSE streaming, web search access, Docker deployment, and client examples for OpenAI SDK-style usage. It is useful for developers who want local experimentation with OpenAI-compatible tooling while routing requests through Gemini web behavior. -
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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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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. -
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ComfyUI IPAdapter plus
ComfyUI reference implementation for IPAdapter models
ComfyUI_IPAdapter_plus is a ComfyUI extension that adds a reference implementation for IPAdapter models. It focuses on image-to-image conditioning, letting a reference image guide the subject, style, or composition of a new generation. The project treats IPAdapter like a one-image LoRA, making it useful when users want visual influence without full model training. It includes example workflows that cover the main IPAdapter functions and help users build practical ComfyUI graphs. The... -
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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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OpenAI
Swift community driven package for OpenAI public API
...The SDK supports a wide range of features including chat completions, embeddings, image generation, audio processing, and structured outputs, making it a comprehensive toolkit for building AI-powered applications. It also includes support for advanced features such as function calling, assistants, and tool integration through protocols like Model Context Protocol, enabling more complex and interactive AI workflows. -
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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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DINOv2
PyTorch code and models for the DINOv2 self-supervised learning
...It builds on the DINO idea of student–teacher distillation and adapts it to modern Vision Transformer backbones with a carefully tuned recipe for data augmentation, optimization, and multi-crop training. The core promise is that a single pretrained backbone can transfer well to many downstream tasks—from linear probing on classification to retrieval, detection, and segmentation—often requiring little or no fine-tuning. The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints.
