This GitHub repository contains a collection of example files demonstrating various use cases and configurations for the llamafiles tools, including examples:
* **System Administration:** Scripts and configurations for Ubuntu, Raspberry Pi 5, and macOS.
* **LLM Interaction:** Examples of prompts and interactions with LLMs like Mixtral and Dolphin.
* **Text Processing:** Scripts for summarizing text, extracting information, and formatting output.
* **Development Tools:** Examples related to Git, Emacs, and other development tools.
* **Hardware Monitoring:** Scripts for monitoring GPU and NVMe drive status.
* **System Administration:** Scripts and configurations for Ubuntu, Raspberry Pi 5, and macOS.
* **LLM Interaction:** Examples of prompts and interactions with LLMs like Mixtral and Dolphin.
* **Text Processing:** Scripts for summarizing text, extracting information, and formatting output.
* **Development Tools:** Examples related to Git, Emacs, and other development tools.
* **Hardware Monitoring:** Scripts for monitoring GPU and NVMe drive status.
An article introducing metap, a new meta-programming layer for Python, detailing its features like program augmentation, code generation, and structural introspection. It also discusses the benefits of metap over DSLs and provides a tutorial with examples.
This article explores gamma spectroscopy using a Radiacode 103G detector and Python, detailing data collection, analysis, and experiments with various objects to identify radioactive elements.
This demo showcases how the Arch can be used to build an HR agent to manage workforce-related inquiries, workforce planning, and communication via Slack. It intelligently routes incoming prompts to the correct targets, providing concise and useful responses tailored for HR and workforce decision-making.
A list of 10 Python libraries that can improve your workflow and make coding easier.
1. **boltons:** Collection of useful Python utilities, filling gaps in the standard library.
2. **tenacity:** Elegant retrying mechanism for functions that might fail.
3. **diskcache:** Persistent caching system backed by a disk (SQLite).
4. **glom:** Toolkit for easily accessing nested data structures (like JSON).
5. **tqdm.contrib.concurrent:** Multi-threaded progress bars using `tqdm`.
6. **anyio:** Compatibility layer for asynchronous Python code across different libraries.
7. **deepdiff:** Detects differences between Python objects (dicts, lists, etc.).
8. **pyrsistent:** Immutable data structures for functional programming.
9. **structlog:** Library for creating structured, parseable logs.
10. **pyinstrument:** Python profiler that generates flamegraphs for performance analysis.
1. **boltons:** Collection of useful Python utilities, filling gaps in the standard library.
2. **tenacity:** Elegant retrying mechanism for functions that might fail.
3. **diskcache:** Persistent caching system backed by a disk (SQLite).
4. **glom:** Toolkit for easily accessing nested data structures (like JSON).
5. **tqdm.contrib.concurrent:** Multi-threaded progress bars using `tqdm`.
6. **anyio:** Compatibility layer for asynchronous Python code across different libraries.
7. **deepdiff:** Detects differences between Python objects (dicts, lists, etc.).
8. **pyrsistent:** Immutable data structures for functional programming.
9. **structlog:** Library for creating structured, parseable logs.
10. **pyinstrument:** Python profiler that generates flamegraphs for performance analysis.
The official Python SDK for Model Context Protocol servers and clients. It allows building MCP clients, servers, and provides tools for interacting with LLMs in a standardized way.
An MCP server that gives language models temporal awareness and time calculation abilities. Teaching AI the significance of the passage of time through collaborative tool development.
This page details the topic namers available in Turftopic, allowing automated assignment of human-readable names to topics. It covers Large Language Models (local and OpenAI), N-gram patterns, and provides API references for the `TopicNamer`, `LLMTopicNamer`, `OpenAITopicNamer`, and `NgramTopicNamer` classes.
Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini. The article details training a FASTopic model and labeling its results using GPT-4.0 mini, emphasizing reproducibility and control over the labeling process.
Turn any Kiwix ZIM archive (offline Wikipedia, Stack Exchange, DevDocs, etc.) into an instant knowledge source for LLMs with a tiny CLI + Python server exposing searchable chunks, metadata and citations.
