Search Results for "simple-xml"
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Showing 45 open source projects for "simple-xml"
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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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1
GPT4All
Run Local LLMs on Any Device. Open-source
GPT4All is an open-source project that allows users to run large language models (LLMs) locally on their desktops or laptops, eliminating the need for API calls or GPUs. The software provides a simple, user-friendly application that can be downloaded and run on various platforms, including Windows, macOS, and Ubuntu, without requiring specialized hardware. It integrates with the llama.cpp implementation and supports multiple LLMs, allowing users to interact with AI models privately. This project also supports Python integrations for easy automation and customization. ... -
2
files-to-prompt
Concatenate a directory full of files into a single prompt
...It includes rich filtering controls, letting you limit by extension, include or skip hidden files, and ignore paths that match glob patterns or .gitignore rules. The output format is flexible: you can emit plain text, Markdown with fenced code blocks, or a Claude-XML style format designed for structured multi-file prompts. It can read file paths from stdin (including NUL-separated paths), which makes it easy to combine with find, rg, or other shell tools. -
3
MiroFish
A Simple and Universal Swarm Intelligence Engine
MiroFish is a next-generation artificial intelligence prediction engine that leverages multi-agent technology and swarm-intelligence simulation to model, simulate, and forecast complex real-world scenarios. The system extracts “seed” information from sources such as breaking news, policy documents, and market signals to construct a high-fidelity digital parallel world populated by thousands of virtual agents with independent memory and behavior rules. Users can inject variables or conditions... -
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LangCheck
Simple, Pythonic building blocks to evaluate LLM applications
Simple, Pythonic building blocks to evaluate LLM applications. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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AirLLM
AirLLM 70B inference with single 4GB GPU
AirLLM is an open source Python library that enables extremely large language models to run on consumer hardware with very limited GPU memory. The project addresses one of the main barriers to local LLM experimentation by introducing a memory-efficient inference technique that loads model layers sequentially rather than storing the entire model in GPU memory. This layer-wise inference approach allows models with tens of billions of parameters to run on devices with only a few gigabytes of... -
6
llm.c
LLM training in simple, raw C/CUDA
...By stripping away heavy frameworks, it exposes the core math and memory flows of embeddings, attention, and feed-forward layers. The code illustrates how to wire forward passes, losses, and simple training or inference loops with direct control over arrays and buffers. Its compact design makes it easy to trace execution, profile hotspots, and understand the cost of each operation. Portability is a goal: it aims to compile with common toolchains and run on modest hardware for small experiments. Rather than delivering a production-grade stack, it serves as a reference and learning scaffold for people who want to “see the metal” behind LLMs. -
7
Index
The SOTA Open-Source Browser Agent
...Instead of writing detailed browser automation code, users can describe the desired task and allow the agent to interpret the page structure, interact with elements, and complete multi-step workflows automatically. The project is built to integrate easily with applications through a simple programming interface, allowing developers to embed browser automation capabilities directly into their software systems. Index can perform tasks such as navigating pages, filling forms, collecting data, and analyzing web content without requiring manual scripting for each website. -
8
Heretic
Fully automatic censorship removal for language models
...Designed for researchers and advanced users, Heretic makes it possible to study and experiment with uncensored model responses in a reproducible, automated way. The project can decensor many popular dense and some mixture-of-experts (MoE) models, supporting workflows that would otherwise require manual tuning. Beyond simple decensoring, Heretic includes research-oriented options for analyzing model internals and interpretability data. -
9
Reader 3
Quick illustration of how one can easily read books together with LLMs
This project is a minimalist, self-hosted EPUB reader designed to help users browse and read EPUB books one chapter at a time through a lightweight local server, making it especially easy to extract or work with chapters in external tools like large language models. It was created primarily as a simple demonstration of how to combine local book reading with LLM workflows without heavy dependencies or complicated setup, and it runs with just a small Python script and a basic HTTP server. The interface focuses on clarity and ease of use, offering straightforward navigation of book chapters rather than full-featured e-reading capabilities. ... -
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10
claude-obsidian
Claude + Obsidian knowledge companion
claude-obsidian is an AI-powered knowledge engine that transforms an Obsidian vault into a self-organizing, continuously evolving wiki. Instead of acting as a simple chat assistant, it autonomously creates, links, and maintains structured knowledge based on user inputs and external sources. The system follows the LLM Wiki pattern, where information is stored as persistent markdown files that grow richer over time through cross-referencing and synthesis. It includes features such as contradiction detection, orphaned note identification, and automatic indexing. ... -
11
SimpleLLM
950 line, minimal, extensible LLM inference engine built from scratch
...It provides the core components of an LLM runtime—such as tokenization, batching, and asynchronous execution—without the abstraction overhead of more complex engines, making it easier for developers and researchers to understand and modify. Designed to run efficiently on high-end GPUs like NVIDIA H100 with support for models such as OpenAI/gpt-oss-120b, Simple-LLM implements continuous batching and event-driven inference loops to maximize hardware utilization and throughput. Its straightforward code structure allows anyone experimenting with custom kernels, new batching strategies, or inference optimizations to trace execution from input to output with minimal cognitive overhead. -
12
BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. It is based on the excellent Tensor2Tensor visualization tool. ... -
13
oterm
the terminal client for Ollama
...The tool allows users to chat with local AI models directly from the terminal without needing a graphical interface or web application. Its interface is designed to be simple and intuitive, enabling developers to launch conversations quickly using a single command. Oterm supports persistent chat sessions that store conversations, system prompts, and parameter configurations locally in a database. This allows users to maintain multiple conversations and reuse previous context across sessions. The tool also integrates with the Model Context Protocol so it can interact with external tools and prompts provided through MCP servers. -
14
Gemma
Gemma open-weight LLM library, from Google DeepMind
Gemma, developed by Google DeepMind, is a family of open-weights large language models (LLMs) built upon the research and technology behind Gemini. This repository provides the official implementation of the Gemma PyPI package, a JAX-based library that enables users to load, interact with, and fine-tune Gemma models. The framework supports both text and multi-modal input, allowing natural language conversations that incorporate visual content such as images. It includes APIs for... -
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Deep Lake
Data Lake for Deep Learning. Build, manage, and query datasets
Deep Lake (or Deeplake, formerly known as Activeloop Hub) is a data lake for deep learning applications. Our open-source dataset format is optimized for rapid streaming and querying of data while training models at scale, and it includes a simple API for creating, storing, and collaborating on AI datasets of any size. It can be deployed locally or in the cloud, and it enables you to store all of your data in one place, ranging from simple annotations to large videos. Deep Lake is used by Google, Waymo, Red Cross, Omdena, Yale, & Oxford. Use one API to upload, download, and stream datasets to/from AWS S3/S3-compatible storage, GCP, Activeloop cloud, or local storage. ... -
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How to Train Your GPT
Build a modern LLM from scratch. Every line commented
How to Train Your GPT is an interactive textbook that teaches users how to build, train, and run a modern language model from scratch. It is written for learners with minimal machine-learning background, using simple explanations, commented code, and practical examples. The project covers the same broad family of architecture behind systems such as GPT-style models, LLaMA-style models, Claude-style systems, and Mistral-style models. It includes chapters and topic explainers on tokenizers, embeddings, attention, RoPE, RMSNorm, SwiGLU, KV cache, AdamW, mixed precision, training loops, and inference. ... -
17
OM1
Modular AI runtime for robots
...The project focuses on creating a modular architecture where language models can coordinate with external tools, APIs, and knowledge sources to accomplish multi-step objectives. Instead of operating as simple conversational systems, OM1 agents can plan actions, retrieve information, and execute tasks across different services. The framework integrates reasoning modules, planning strategies, and tool interfaces that allow agents to operate in dynamic environments. Developers can extend the system by connecting new tools, services, or data sources to the agent architecture. ... -
18
VLMEvalKit
Open-source evaluation toolkit of large multi-modality models (LMMs)
...The toolkit provides a unified framework that allows researchers and developers to evaluate multimodal models across a wide range of datasets and standardized benchmarks with minimal setup. Instead of requiring complex data preparation pipelines or multiple repositories for each benchmark, the system enables evaluation through simple commands that automatically handle dataset loading, model inference, and metric computation. VLMEvalKit supports generation-based evaluation methods, allowing models to produce textual responses to visual inputs while measuring performance through techniques such as exact matching or language-model-assisted answer extraction. -
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Context Engineering
A frontier, first-principles handbook
Context Engineering is a comprehensive, open-source project serving as a first-principles handbook for the emerging discipline of context design and optimization in AI. Moving beyond traditional prompt engineering, this repository defines and explores how to craft and provide complete context payloads — not just single prompts — to large language models so they can perform tasks more reliably and intelligently. It takes inspiration from thought leaders like Andrej Karpathy and bridges theory... -
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AI Engineering Hub
In-depth tutorials on LLMs, RAGs and real-world AI agent applications
...It includes more than 90 production-ready projects across skill levels, organized into beginner, intermediate, and advanced categories to guide users progressively from simple experiments to complex AI workflows. Projects range from OCR applications and local chatbot UIs to multimodal RAG systems and multi-agent automation pipelines, making the hub valuable both as a learning resource and as a practical reference. The repository provides in-depth notebooks, example code, and integration patterns that illustrate how to implement, adapt, and scale AI features in real applications. -
21
TokenCost
Easy token price estimates for 400+ LLMs. TokenOps
TokenCost is an open-source developer utility designed to estimate the cost of using large language model APIs by calculating token usage and translating it into real monetary values. The tool focuses on helping developers understand how much their prompts and generated completions cost when interacting with commercial AI models. It works by counting tokens in prompts and responses before or after sending requests and then applying pricing information associated with different models. This... -
22
nano-graphrag
A simple, easy-to-hack GraphRAG implementation
nano-graphrag is a lightweight implementation of the GraphRAG approach designed to simplify experimentation with graph-based retrieval-augmented generation systems. GraphRAG expands traditional RAG pipelines by constructing knowledge graphs from documents and using relationships between entities to improve the quality and reasoning of AI responses. The nano-GraphRAG project focuses on reducing complexity by providing a compact and readable codebase that preserves the core functionality of... -
23
LangBot
Production-grade platform for building agentic IM bots
...It supports numerous messaging services including Discord, Slack, Telegram, WeChat, and other enterprise communication systems, making it a flexible solution for both personal projects and organizational deployments. LangBot combines LLM capabilities with agent logic, knowledge base orchestration, and plugin infrastructure so that bots can perform complex tasks rather than simple conversational responses. The platform includes a web-based management interface that simplifies configuration, access control, and integration with external AI services. -
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AReal
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible
AReaL is an open source, fully asynchronous reinforcement learning training system. AReal is designed for large reasoning and agentic models. It works with models that perform reasoning over multiple steps, agents interacting with environments. It is developed by the AReaL Team at Ant Group (inclusionAI) and builds upon the ReaLHF project. Release of training details, datasets, and models for reproducibility. It is intended to facilitate reproducible RL training on reasoning / agentic tasks,... -
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Kaleidoscope-SDK
User toolkit for analyzing and interfacing with Large Language Models
kaleidoscope-sdk is a Python module used to interact with large language models hosted via the Kaleidoscope service available at: https://github.com/VectorInstitute/kaleidoscope. It provides a simple interface to launch LLMs on an HPC cluster, asking them to perform basic features like text generation, but also retrieve intermediate information from inside the model, such as log probabilities and activations. Users must authenticate using their Vector Institute cluster credentials. This can be done interactively instantiating a client object. ...
