Search Results for "model-builder" - Page 12
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Showing 1291 open source projects for "model-builder"
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tiny-llm
A course of learning LLM inference serving on Apple Silicon
tiny-llm is an educational open-source project designed to teach system engineers how large language model inference and serving systems work by building them from scratch. The project is structured as a guided course that walks developers through the process of implementing the core components required to run a modern language model, including attention mechanisms, token generation, and optimization techniques. Rather than relying on high-level machine learning frameworks, the codebase uses mostly low-level array and matrix manipulation APIs so that developers can understand exactly how model inference works internally. ... -
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Qwen3 Embedding
Designed for text embedding and ranking tasks
Qwen3-Embedding is a model series from the Qwen family designed specifically for text embedding and ranking tasks. It builds upon the Qwen3 base/dense models and offers several sizes (0.6B, 4B, 8B parameters), for both embedding and reranking, with high multilingual capability, long‐context understanding, and reasoning. It achieves state-of-the-art performance on benchmarks like MTEB (Multilingual Text Embedding Benchmark) and supports instruction-aware embedding (i.e. embedding task instructions along with queries) and flexible embedding/vector dimension definitions. ... -
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
...Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and deployment (CI/CD) tools to scale and update your deployment. Built on Kubernetes, runs on any cloud and on-premises. Framework agnostic, supports top ML libraries, toolkits and languages. ... -
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WhisperLive
A nearly-live implementation of OpenAI's Whisper
...Configuration options let you control the number of clients, maximum connection time, and threading behavior so the server can be tuned for different deployment environments. On the client side, you can set the language, whether to translate into English, model size, voice activity detection, and output recording behavior. -
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VikingDB MCP Server
A mcp server for vikingdb store and search
An MCP server that interfaces with VikingDB, a high-performance vector database developed by ByteDance, enabling efficient vector storage and search capabilities. -
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Llama Stack
Composable building blocks to build Llama Apps
Llama-Stack is an open-source framework designed to facilitate the deployment and fine-tuning of large language models (LLMs) for various natural language processing tasks. -
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Agent S
Agent S: an open agentic framework that uses computers like a human
...Agent S combines powerful foundation models (such as GPT-5) with grounding models like UI-TARS to translate visual inputs into precise executable actions. It supports flexible deployment via CLI, SDK, or cloud, and integrates with multiple model providers including OpenAI, Anthropic, Gemini, Azure, and Hugging Face endpoints. With optional local code execution, reflection mechanisms, and compositional planning, Agent S provides a scalable and research-driven framework for building advanced computer-use agents. -
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segment-geospatial
A Python package for segmenting geospatial data with the SAM
The segment-geospatial package draws its inspiration from segment-anything-eo repository authored by Aliaksandr Hancharenka. To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I have adapted the source code of segment-geospatial from the segment-anything-eo repository, and credit for its original version goes to Aliaksandr Hancharenka. -
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MiniMax-M1
Open-weight, large-scale hybrid-attention reasoning model
MiniMax-M1 is presented as the world’s first open-weight, large-scale hybrid-attention reasoning model, designed to push the frontier of long-context, tool-using, and deeply “thinking” language models. It is built on the MiniMax-Text-01 foundation and keeps the same massive parameter budget, but reworks the attention and training setup for better reasoning and test-time compute scaling. Architecturally, it combines Mixture-of-Experts layers with lightning attention, enabling the model to support a native context length of 1 million tokens while using far fewer FLOPs than comparable reasoning models for very long generations. ... -
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WeKnora
LLM framework for document understanding and semantic retrieval
...It follows the Retrieval-Augmented Generation (RAG) paradigm, where relevant document segments are retrieved and used by language models to generate accurate, context-aware responses. This approach enables the system to provide more reliable answers by grounding model reasoning in the content of uploaded documents. WeKnora is designed with a modular architecture that separates components for document processing, search strategies, and model inference, allowing developers to customize or extend different parts of the pipeline. It supports knowledge base management and conversational question answering built on top of structured and unstructured documents. -
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LLM Council
LLM Council works together to answer your hardest questions
LLM Council is a creative open-source web application by Andrej Karpathy that lets you consult multiple large language models together to answer questions more reliably than querying a single model. Instead of relying on one provider, this application sends your query simultaneously to several LLMs supported via OpenRouter, collects each model’s independent response, and then orchestrates a multi-stage evaluation where the models critique and rank each other’s outputs anonymously. After this peer-review process, a designated “Chairman” model synthesizes a final consolidated answer drawing on the strengths and insights of all participants. ... -
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Google DeepMind GraphCast and GenCast
Global weather forecasting model using graph neural networks and JAX
...Both models are built on JAX and integrate advanced neural architectures capable of learning from multi-scale geophysical data represented on icosahedral meshes. The package includes pretrained model weights, normalization statistics, and demonstration notebooks that allow users to replicate and fine-tune weather forecasting experiments in Colab or on Google Cloud TPUs and GPUs. -
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BudouX
Standalone, small, language-neutral
...It works with no dependency on third-party word segmenters such as Google cloud natural language API. It is small. It takes only around 15 KB including its machine learning model. It's reasonable to use it even on the client-side. It is language-neutral. You can train a model for any language by feeding a dataset to BudouX’s training script. -
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Pytorch-toolbelt
PyTorch extensions for fast R&D prototyping and Kaggle farming
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming. Easy model building using flexible encoder-decoder architecture. Modules: CoordConv, SCSE, Hypercolumn, Depthwise separable convolution and more. GPU-friendly test-time augmentation TTA for segmentation and classification. GPU-friendly inference on huge (5000x5000) images. Every-day common routines (fix/restore random seed, filesystem utils, metrics). ... -
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AUTOMATIC1111 Stable Diffusion web UI
Stable Diffusion web UI
AUTOMATIC1111's stable-diffusion-webui is a powerful, user-friendly web interface built on the Gradio library that allows users to easily interact with Stable Diffusion models for AI-powered image generation. Supporting both text-to-image (txt2img) and image-to-image (img2img) generation, this open-source UI offers a rich feature set including inpainting, outpainting, attention control, and multiple advanced upscaling options. With a flexible installation process across Windows, Linux, and... -
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bert4torch
An elegent pytorch implement of transformers
An elegant PyTorch implement of transformers. -
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Fli
Google Flights MCP and Python Library
...The library supports a wide range of flight search capabilities, including filtering by airline, departure time, number of stops, cabin class, and sorting by price or duration, making it suitable for both casual queries and advanced travel analysis. In addition to its CLI interface, fli includes a Model Context Protocol (MCP) server that allows AI assistants to interact with flight data using structured tools, enabling natural language queries and automation workflows. -
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MCP for Unity
AI bridge enabling assistants to control and automate Unity Editor
Unity MCP is an open source integration that connects AI assistants with the Unity Editor through the Model Context Protocol (MCP). It acts as a bridge that allows language models and AI coding tools to interact directly with a Unity development environment using structured commands and tools. By linking an AI assistant to a running Unity project, the system enables automated operations such as managing project assets, modifying scenes, editing scripts, and performing other development tasks inside the editor. ... -
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Open-AutoGLM
An open phone agent model & framework
Open-AutoGLM is an open-source framework and model designed to empower autonomous mobile intelligent assistants by enabling AI agents to understand and interact with phone screens in a multimodal manner, blending vision and language capability to control real devices. It aims to create an “AI phone agent” that can perceive on-screen content, reason about user goals, and execute sequences of taps, swipes, and text input via automated device control interfaces like ADB, enabling hands-off completion of multi-step tasks such as navigating apps, filling forms, and more. ... -
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Audiblez
Generate audiobooks from e-books
Audiblez is a tool for generating high-quality .m4b audiobooks directly from .epub e-books using the Kokoro-82M neural text-to-speech model. It focuses on making audiobook creation easy and fast: from a single command, the tool splits an e-book into chapters, synthesizes audio for each section, and then merges the results into a structured audiobook with chapter-based WAV files and a final .m4b container. The Kokoro-82M model it uses is compact (82M parameters) yet natural sounding, trained on under 100 hours of audio, and supports multiple languages, including English (US/UK), Spanish, French, Hindi, Italian, Japanese, Brazilian Portuguese, and Mandarin Chinese. ... -
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OpenCompass
OpenCompass is an LLM evaluation platform
...With its powerful algorithms and intuitive interface, OpenCompass makes it easy to assess the quality and effectiveness of your NLP models. OpenCompass is a one-stop platform for large model evaluation, aiming to provide a fair, open, and reproducible benchmark for large model evaluation. Pre-support for 20+ HuggingFace and API models, a model evaluation scheme of 50+ datasets with about 300,000 questions, comprehensively evaluating the capabilities of the models in five dimensions. One line command to implement task division and distributed evaluation, completing the full evaluation of billion-scale models in just a few hours. ... -
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Deepchecks
Test Suites for validating ML models & data
...Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. To run a specific single check, all you need to do is import it and then to run it with the required (check-dependent) input parameters. More details about the existing checks and the parameters they can receive can be found in our API Reference. ... -
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X-AnyLabeling
Effortless data labeling with AI support from Segment Anything
X-AnyLabeling is an open-source data annotation platform designed to streamline the process of labeling datasets for computer vision and multimodal AI applications. The software integrates an AI-powered labeling engine that allows users to generate annotations automatically with the assistance of modern vision models such as Segment Anything and various object detection frameworks. It supports labeling tasks across images and videos and enables developers to prepare training datasets for... -
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MindsDB
Making Enterprise Data Intelligent and Responsive for AI
MindsDB is an AI data solution that enables humans, AI, agents, and applications to query data in natural language and SQL, and get highly accurate answers across disparate data sources and types. MindsDB connects to diverse data sources and applications, and unifies petabyte-scale structured and unstructured data. Powered by an industry-first cognitive engine that can operate anywhere (on-prem, VPC, serverless), it empowers both humans and AI with highly informed decision-making... -
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Wanwu AI Agent Platform
Enterprise AI agent platform for workflows, models, and RAG apps
...Wanwu integrates large language models with business process automation, allowing developers to design complex, production-ready AI solutions tailored to enterprise needs. It includes comprehensive model lifecycle management capabilities, enabling users to configure, monitor, and manage different models efficiently. Wanwu also supports knowledge base construction, allowing organizations to incorporate structured and unstructured data into their AI applications. With a focus on openness and extensibility, it encourages developers to build on top of its ecosystem while maintaining a secure and compliant architecture for business use cases.
