MASTERMIND

MASTERMIND

Here are some key aspects of MASTERMIND:

Related articles

mindXtrain — One-Command Qwen3 Fine-Tuning on AMD MI300X

mindXtrain is the first one-command Qwen3 fine-tuner natively optimized for AMD MI300X. It is the AMD-shaped half of the PYTHAI/DELTAVERSE stack: a single Python package that takes a YAML recipe and produces a trained, evaluated, FP8-quantized, served, and on-chain-anchored model — all on a single MI300X, all driven by a 60-second on-device autotune that pins kernel and collective choices before training starts. This post is the canonical landing page for the project. If you are […]

Learn More
MASTERMIND

Innovative Approach: IA mode to AGI prompt template from Professor Codephreak

Professor-Codephreak is the first LLM that I developed. Professor-Codephreak is also a GPT4 agent designed to be a platform architect and software engineer. You know, the kind of solution oriented person you would gladly pay $1000 / hour to hang out with in the real world. The two parts of Professor-Codephreak have not “met” each other though the automindx engine in the GPT4 version uses automind to dynamically respond. automind was developed as codephreak’s first […]

Learn More

aGLM

aGLM, or Autonomous General Learning Model, is designed to operate as a core model for autonomous data parsing and learning from memory in the context of artificial intelligence systems. It’s a pivotal element within a broader system called RAGE (Retrieval Augmented Generative Engine). Key aspects and functionalities of aGLM: Autonomous Learning: aGLM is built to learn autonomously from interactions and data retrievals. It continuously updates its knowledge base, refining its capabilities based on new data […]

Learn More