algorythmtechnologies/RR_V1_UNTRAINED

AlgoRythm Prandtl Aero

Computational Engineering Model (CEM) - Mission Critical

A deterministic AI system for rocket engine design, built on Qwen2.5-Coder-7B-Instruct

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                    AlgoRythm Prandtl Aero Engineering
                    MISSION CRITICAL EDITION (7B)

Overview

AlgoRythm Prandtl Aero transforms a high-reasoning LLM into a deterministic physics-based design engine.

• Base Model: Qwen2.5-Coder-7B-Instruct (High reasoning capacity)

• Capability: >50% of Noyron

• Logic: System Architecture & Component Design

• Safety: Physics-validated, deterministic output

• Calculates using real physics equations

• Validates against physical constraints

• Generates executable PicoGK C# code

• Never guesses - always derives

Project Structure

AlgoRythm_PrandtlAero/
├── datasets/           # Training data
│   ├── rocket_propulsion_dataset.json
│   ├── thermal_fluid_dataset.json
│   ├── advanced_structures_dataset.json
│   └── generate_dataset.py
├── training/           # Fine-tuning scripts
│   └── train_full.py
├── inference/          # Run the model
│   └── run_inference.py
├── physics_engine/     # Validation
│   └── validator.py
├── model/              # Saved models
├── ARCHITECTURE.md     # System diagrams
└── model_config.json   # Configuration

Quick Start

1. Generate Training Dataset

cd AlgoRythm_PrandtlAero/datasets
python generate_dataset.py  # Creates 500 examples

2. Train on H100/A100 (1.5-2 hours)

cd AlgoRythm_PrandtlAero/training
python train_full.py

3. Run Inference

cd AlgoRythm_PrandtlAero/inference
python run_inference.py

Training Configuration

Parameter	Value	Rationale
Epochs	4	Small model needs repetition
Learning Rate	2e-5	Preserve coding knowledge
Batch Size	4	Fits H100 memory
Context Length	8192	Long engineering problems
LoRA Rank	64	Strong adaptation
Temperature	0.0	Deterministic output

Dataset Format

Each training example follows Chain-of-Logic structure:

{
  "input": "Design 5kN LOX/LH2 nozzle at 100 bar",
  "reasoning": "[PHYSICS_DERIVATION]...",
  "output": "using PicoGK; ..."
}

Comparison with Noyron

Feature	Noyron	Prandtl Aero
Architecture	Proprietary	Open (Fine-tuned LLM)
Physics	Encoded rules	Learned + validated
Output	PicoGK code	PicoGK code
Deterministic	Yes	Yes (temp=0)
Training	N/A	2 hrs on H100

License

Apache 2.0 - Built by AlgoRythm Prandtl Aero Engineering from AlgoRythm Group