Search Results for "apostila-python" - Page 22
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Showing 2845 open source projects for "apostila-python"
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1
Gitingest
Create prompt-friendly codebase digests from any Git repository URL
...In addition to producing the code digest, Gitingest also calculates statistics about the extracted content such as repository structure, total size of the extract, and token count. Gitingest can be used as a command line utility or integrated directly into Python applications. -
2
AIDE ML
AI-Driven Exploration in the Space of Code
...The system repeatedly improves its generated code by exploring different implementation paths and selecting the best-performing solutions. AIDE ML is packaged as a Python toolkit with built-in utilities such as command-line tools, configuration presets, and visualization interfaces that allow researchers to observe how the search process evolves. The framework is designed for experimentation and academic research into automated programming and machine learning optimization. -
3
E2B Desktop Sandbox
E2B Desktop Sandbox for LLMs. E2B Sandbox
...Each sandbox runs independently and can be configured with custom dependencies or tools required by an AI agent or automation workflow. The system allows developers to programmatically create and control these virtual desktops through SDKs available in languages such as Python and JavaScript. Within a sandbox, developers can launch applications like browsers, editors, or other software that an AI agent may need to interact with. This approach is particularly useful for building AI agents capable of interacting with graphical environments or performing tasks such as browsing, testing software, or automating workflows. -
4
Chat with LLMs Everywhere
Run PyTorch LLMs locally on servers, desktop and mobile
...It is intended primarily as a reference implementation that shows developers how to integrate large language models into applications without requiring a large or complex infrastructure stack. TorchChat supports running models through Python interfaces as well as integrating them directly into native applications written in languages such as C or C++. The project also demonstrates how modern LLMs like LLaMA-style models can be deployed locally while maintaining good performance across different hardware platforms. -
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5
LightLLM
LightLLM is a Python-based LLM (Large Language Model) inference
...The framework enables developers to run and serve modern language models with significantly improved speed and resource efficiency compared to many traditional inference systems. Built primarily in Python, the project integrates optimization techniques and ideas from several leading open-source implementations, including FasterTransformer, vLLM, and FlashAttention, to accelerate token generation and reduce latency. LightLLM is designed to handle large-scale model workloads in production environments, supporting efficient batching and GPU utilization for fast inference across multiple requests. ... -
6
Nano-vLLM
A lightweight vLLM implementation built from scratch
Nano-vLLM is a lightweight implementation of the vLLM inference engine designed to run large language models efficiently while maintaining a minimal and readable codebase. The project recreates the core functionality of vLLM in a simplified architecture written in approximately a thousand lines of Python, making it easier for developers and researchers to understand how modern LLM inference systems work. Despite its compact design, nano-vllm incorporates advanced optimization techniques such as prefix caching, tensor parallelism, and CUDA graph execution to achieve high performance during model inference. The engine is intended primarily for educational use, experimentation, and lightweight deployments where a full production-grade inference stack may be unnecessary. ... -
7
VibeTensor
Our first fully AI generated deep learning system
...What makes VibeTensor remarkable is that every major component, from core libraries and dispatch systems to CUDA runtime support, caching allocators, and language bindings, was created and validated by coding agents using automated builds and tests rather than manual line-by-line human coding. The system includes both a Python frontend via a torch-like API and an experimental Node.js/TypeScript interface. -
8
Agent Reinforcement Trainer
Train multi-step agents for real-world tasks using GRPO
...Instead of just manually crafting prompts or relying on supervised fine-tuning, ART uses techniques like Group Relative Policy Optimization (GRPO) to let agents learn from environmental feedback and reward signals. The framework is designed to integrate easily with Python applications, abstracting much of the RL infrastructure so developers can train agents without deep RL expertise or heavy infrastructure overhead. ART also supports scalable training patterns, observability tools, and integration with hosted platforms like Weights & Biases, and it provides notebooks that demonstrate training on standard benchmarks and tasks. -
9
Live Agent Studio
Open source AI Agents hosted on the oTTomator Live Agent Studio
...The repository is community focused, with sample agents like tweet generators, smart selectors, research assistants, and multi-tool workflows that show how agents can integrate with tools like n8n or custom Python code. Because it’s tied to the broader Live Agent Studio ecosystem, users can experiment with deploying and using these agents in a hosted environment. -
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10
Granite TSFM
Foundation Models for Time Series
...The repository focuses on end-to-end workflows: loading data, building datasets, fine-tuning forecasters, running evaluations, and serving models. It documents the currently supported Python versions and points users to where the core TSFM models are hosted and how to wire up service components. Issues and examples in the tracker illustrate common tasks such as slicing inference windows or using pipeline helpers that return pandas DataFrames, grounding the library in day-to-day time-series operations. The ecosystem around TSFM also includes a community cookbook of “recipes” that showcase capabilities and patterns. ... -
11
xFormers
Hackable and optimized Transformers building blocks
...One of its key goals is efficient attention: it supports dense, sparse, low-rank, and approximate attention mechanisms (e.g. FlashAttention, Linformer, Performer) via interchangeable modules. The library includes memory-efficient operator implementations in both Python and optimized C++/CUDA, ensuring that performance isn’t sacrificed for modularity. It also integrates with PyTorch seamlessly so you can drop in its blocks to existing models, replace default attention layers, or build new architectures from scratch. xformers includes training, deployment, and memory profiling tools. -
12
Telegram Media Downloader
Download media files from a telegram conversation/chat/channel
Download media files from a telegram conversation/chat/channel up to 2GiB per file. -
13
TorchIO
Medical imaging toolkit for deep learning
...TorchIO is a Python package containing a set of tools to efficiently read, preprocess, sample, augment, and write 3D medical images in deep learning applications written in PyTorch, including intensity and spatial transforms for data augmentation and preprocessing. Transforms include typical computer vision operations such as random affine transformations and also domain-specific ones such as simulation of intensity artifacts due to MRI magnetic field inhomogeneity. -
14
tf2onnx
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
tf2onnx converts TensorFlow (tf-1.x or tf-2.x), keras, tensorflow.js and tflite models to ONNX via command line or python API. Note: tensorflow.js support was just added. While we tested it with many tfjs models from tfhub, it should be considered experimental. TensorFlow has many more ops than ONNX and occasionally mapping a model to ONNX creates issues. tf2onnx will use the ONNX version installed on your system and installs the latest ONNX version if none is found. -
15
Haiku
JAX-based neural network library
...It preserves Sonnet’s module-based programming model for state management while retaining access to JAX’s function transformations. Haiku can be expected to compose with other libraries and work well with the rest of JAX. Similar to Sonnet modules, Haiku modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs. -
16
DeepLabCut
Implementation of DeepLabCut
...This package is collaboratively developed by the Mathis Group & Mathis Lab at EPFL (releases prior to 2.1.9 were developed at Harvard University). The code is freely available and easy to install in a few clicks with Anaconda (and pypi). DeepLabCut is an open-source Python package for animal pose estimation. -
17
ClearML
Streamline your ML workflow
...It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments and other workflows with ClearML powerful and versatile set of classes and methods. The ClearML Server storing experiment, model, and workflow data, and supports the Web UI experiment manager, and ML-Ops automation for reproducibility and tuning. ... -
18
tvm
Open deep learning compiler stack for cpu, gpu, etc.
...The vision of the Apache TVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. Compilation of deep learning models in Keras, MXNet, PyTorch, Tensorflow, CoreML, DarkNet and more. Start using TVM with Python today, build out production stacks using C++, Rust, or Java the next day. -
19
spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. ... -
20
LangChain MCP
Model Context Protocol tool support for LangChain
langchain-mcp provides Model Context Protocol (MCP) tool support for LangChain, a framework for developing applications powered by language models. It allows developers to create an MCPToolkit with a client. -
21
Logfire MCP
The Logfire MCP Server is here
The Logfire MCP Server is a Model Context Protocol server that allows AI applications to access OpenTelemetry traces and metrics sent to Logfire. It enables retrieval and analysis of telemetry data, enhancing debugging and observability workflows. -
22
Zettelkasten MCP
Implements the Zettelkasten knowledge management methodology
The Zettelkasten MCP Server is a Model Context Protocol (MCP) server that implements the Zettelkasten knowledge management methodology. It allows users to create, link, and manage notes, facilitating a structured and interconnected note-taking system. -
23
LangCheck
Simple, Pythonic building blocks to evaluate LLM applications
Simple, Pythonic building blocks to evaluate LLM applications. -
24
MLE-Agent
Intelligent companion for seamless AI engineering and research
MLE-Agent is designed as a pairing LLM agent for machine learning engineers and researchers. A library designed for managing machine learning experiments, tracking metrics, and model deployment. -
25
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 answering by leveraging both breadth (multiple attempts) and depth (iterative refinement) strategies to find better outputs dynamically
