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Showing 13 open source projects for "x-mind"
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Patching and Endpoint Management for Under $1/EndpointSplashtop AEM gives IT teams automated patching, monitoring, and unattended remote access for Windows and macOS. No long setup, no paying extra for key features.
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1
TreeAI
Branch conversations instead of flattening them
...Sessions, model configurations, and node positions are stored locally in the browser through IndexedDB. Entire conversation trees can be exported as FreeMind-compatible mind maps. Built with React, TypeScript, and React Flow, TreeAI supports visual experimentation and comparison across language models. -
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Anything to NotebookLM
Multi-source content processor for NotebookLM
...It is built for users who want to convert articles, web pages, videos, PDFs, office files, podcasts, images, and search results into more usable study or presentation formats. The project uses natural-language commands, so the user can ask for a podcast, slide deck, mind map, report, quiz, flashcards, or infographic without manually building the workflow. It supports multilingual material, with especially strong use cases for Chinese and English content. The tool can process files locally, extract or transcribe content when needed, and hand the cleaned material to NotebookLM for generation. It is best suited for researchers, students, content curators, and knowledge workers who regularly turn scattered information into organized learning assets. -
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Engram
A New Axis of Sparsity for Large Language Models
...It provides utilities to generate embeddings from text or other structured data, index them using efficient approximate nearest neighbor algorithms, and perform real-time similarity queries even on large corpora. Engineered with speed and memory efficiency in mind, Engram supports batched indexing, incremental updates, and custom distance metrics so developers can tailor search behaviors to their domain’s needs. In addition to raw similarity search, the project includes tools for clustering, ranking, and filtering results, enabling richer user experiences like “related content”, semantic auto-completion, and contextual filtering. -
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Rocketnotes
AI-powered markdown editor - leverage LLMs with your documents
...Instead of functioning purely as a document editor, RocketNotes integrates AI capabilities that assist with tasks such as generating insights, organizing ideas, and improving written content. The system is designed with productivity workflows in mind, allowing users to build interconnected notes and maintain organized collections of information. By combining note management with AI-assisted writing and summarization tools, the platform aims to help users process large volumes of information more efficiently. -
99.99% Uptime for MySQL and PostgreSQL DatabasesCloud SQL Enterprise Plus delivers near-zero downtime with 35 days of point-in-time recovery. Supports MySQL, PostgreSQL, and SQL Server.
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Paddler
Open-source LLM load balancer and serving platform for hosting LLMs
...It supports running models locally through engines such as llama.cpp while distributing requests across multiple compute nodes to improve performance and reliability. The architecture is designed with privacy and cost control in mind, making it suitable for organizations that handle sensitive data or require predictable operational costs. Paddler also includes tools for monitoring, request buffering, and autoscaling integration so that deployments can adapt dynamically to changing workloads. A built-in administrative interface allows developers and operations teams to manage models, observe system performance, and test inference endpoints. -
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rwkv.cpp
INT4/INT5/INT8 and FP16 inference on CPU for RWKV language model
Besides the usual FP32, it supports FP16, quantized INT4, INT5 and INT8 inference. This project is focused on CPU, but cuBLAS is also supported. RWKV is a novel large language model architecture, with the largest model in the family having 14B parameters. In contrast to Transformer with O(n^2) attention, RWKV requires only state from the previous step to calculate logits. This makes RWKV very CPU-friendly on large context lengths. -
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LLaMA Models
Utilities intended for use with Llama models
...It complements separate repos that carry code and demos (for example inference kernels or cookbook content) by keeping authoritative metadata and specs here. Model lineages and size variants are documented externally (e.g., Llama 3.x and beyond), with this repo providing the “single source of truth” links and utilities. In practice, teams use llama-models as a reference when selecting variants, aligning licenses, and wiring in helper scripts for deployment. -
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CodeGeeX
CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)
...Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art performance compared to other open models like InCoder and CodeGen. CodeGeeX also powers IDE plugins for VS Code and JetBrains, offering features like code completion, translation, debugging, and annotation. The model supports Ascend 910 and NVIDIA GPUs, with optimizations like quantization and FasterTransformer acceleration for faster inference. -
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CodeGeeX2
CodeGeeX2: A More Powerful Multilingual Code Generation Model
CodeGeeX2 is the second-generation multilingual code generation model from ZhipuAI, built upon the ChatGLM2-6B architecture and trained on 600B code tokens. Compared to the first generation, it delivers a significant boost in programming ability across multiple languages, outperforming even larger models like StarCoder-15B in some benchmarks despite having only 6B parameters. The model excels at code generation, translation, summarization, debugging, and comment generation, and it supports... -
PRTG Catches Network Issues Before They Cause DowntimeReactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
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10
Automated Interpretability
Code for Language models can explain neurons in language models paper
...It includes a “neuron explainer” component that, given a target neuron or latent feature, proposes natural language explanations or heuristics (e.g. “this neuron activates when the input has property X”) and then simulates activation behavior across example inputs to test whether the explanation holds. The project also contains a “neuron viewer” web component for browsing neurons, explanations, and activation patterns, making it more interactive and exploratory. -
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llm-chain
Rust crate for building chains in large language models
We offer a collection of Rust crates packed with features that make working with Large Language Models easy and seamless. With llm-chain, you can focus on building powerful AI applications. Create reusable and easily customizable prompt templates for consistent and structured interactions with LLMs. Build powerful chains of prompts that allow you to execute more complex tasks, step by step, leveraging the full potential of LLMs. Provides seamless integration with LLaMa models, enabling... -
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LM Human Preferences
Code for the paper Fine-Tuning Language Models from Human Preferences
...The code is provided “as is” and explicitly says it may no longer run out-of-the-box due to dependencies or dataset migrations. It was tested on the smallest GPT-2 (124M parameters) under a specific environment (TensorFlow 1.x, specific CUDA / cuDNN combinations). It includes utilities for launching experiments, sampling from policies, and simple experiment orchestration. -
13
ImPromptu
Domain Agnostic Prompts for Savvy Professionals
...If that page is empty, then you can help by adding prompts to that page. If you are not sure how to do that, you can read the contributing guidelines. If you are feeling like having your mind melt into magic today then head over to the prompt generator and let the magic happen. This script will literally write your prompts for you, as if chatGPT wasn't enough magic for you already.
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