RHAIIS docs improvements

Develop user guides for building AI agents with LangChain/LangGraph

Create practical guides showing common end-user scenarios for building AI agents using LangChain and LangGraph frameworks with RHAIIS.

Create Jupyter notebook examples for RHAIIS enablement

Develop interactive Jupyter notebooks demonstrating key RHAIIS features and workflows, similar to the reference example (https://colab.research.google.com/drive/1JnVdTtIPC2M0ybD2Tz06HEctiLEak0Vw).

aireilly / llm-compressor-v0.8.0-release-notes.md

Last active October 1, 2025 18:29

LLM Compressor v0.8.0 release notes

This LLM Compressor v0.8.0 release introduces the following new features and enhancements:

Support for multiple modifiers in oneshot compression runs
Quantization and calibration support for Qwen3 models including FP8 quantization support for Qwen3 VL MoE models
Transforms support for non-full-size rotation sizes
Improved accuracy recovery by updating W4A16 schemes to use actorder "weight" by default

Support for multiple modifiers in oneshot compression runs ✨

aireilly / llm-compressor-0.7.0-release-notes.md

Last active August 19, 2025 11:55

llm-compressor v0.7.0 release notes

LLM Compressor v0.7.0 release notes

This LLM Compressor v0.7.0 release introduces major new features and enhancements:

Apply multiple compressors to a single model for mixed-precision quantization
Support for DeepSeekV3-style block FP8 quantization
Configurable Observer arguments in config.yaml
Expanded Mixture of Experts (MoE) calibration support, including NVFP4
Llama4 quantization support with vLLM compatibility
Simplified and unified Recipe classes for easier usage and debugging

aireilly / llm-compressor-toc.md

Created June 25, 2025 09:21

Proposed plan for building out LLM Compressor docs

LMM Compressor docs proposal

About large language model optimization
- Supported model types for optimization
Deciding on a compression strategy
- Supported compression algorithms and formats
- Advanced compression techniques and features
Evaluating model quality

aireilly / ccs-configuring-vscode-for-vibecoding.adoc

Last active April 17, 2025 08:14

Vibecoding JIRA, GitHub, and local file systems with VS Code and MCP

Configuring VS Code and MCP to vibe with JIRA, GitHub, and local file systems

This gist describes how to configure VS Code to use Red Hat approved language models with the following MCP (Model Context Protocol) servers:

Configuring VS Code to use a Red Hat approved language model

aireilly / pr.md

Created March 11, 2022 13:38 — forked from piscisaureus/pr.md

Checkout github pull requests locally

Locate the section for your github remote in the .git/config file. It looks like this:

[remote "origin"]
	fetch = +refs/heads/*:refs/remotes/origin/*
	url = [email protected]:joyent/node.git

Now add the line fetch = +refs/pull/*/head:refs/remotes/origin/pr/* to this section. Obviously, change the github url to match your project's URL. It ends up looking like this:

Aidan Reilly aireilly