[WIP] Set up training pipeline for OpenMythos and fix critical bugs

[Copilot]

Coding agent has begun work on OpenMythos Training System Setup and Bug Fixes and will update this pull request as work progresses.

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The user has attached the following files from their workspace:

• PROGRESS_REVIEW.txt

OpenMythos Training System - Complete Summary

TITLE: OpenMythos Training System Setup and Bug Fixes

USER INTENT:

Set up a complete training pipeline for the OpenMythos RDT (Recurrent-Depth Transformer) model, fix critical bugs, install dependencies, and make the system production-ready for both local and Azure cloud training.

TASK DESCRIPTION:

• Fix syntax errors and bugs in training orchestration scripts
• Install PyTorch, Transformers, and related dependencies in a Kali Linux environment
• Configure Azure CLI for cloud training
• Create automated training scripts for local CPU/GPU and Azure Spot VM training
• Validate the OpenMythos 3B model (3.04 billion parameters)
• Establish complete documentation and quick-start guides

EXISTING:

Successfully Fixed Bugs:

1. preflight_check.py (Line 40, 229):

   • Fixed class name typo: PrefightValidation → PreflightValidation
   • Updated instantiation to match corrected class name

2. azure_train_production.py (Line 299):

   • Fixed operator precedence: if result and "K80" in result or "GPU" in result → if result and ("K80" in result or "GPU" in result)
   • Prevents incorrect boolean evaluation in GPU detection

3. azure_train_production.py (Lines 438-443):

   • Added safety checks for GPU output parsing
   • Validates array length before unpacking to prevent crashes

Installed Dependencies:

• Python 3.13.12 in virtual environment (venv/)
• PyTorch 2.11.0+cu130 (CUDA 13.0 support)
• Transformers 5.7.0
• Datasets 4.8.5 (streaming support)
• Loguru (advanced logging)
• Tiktoken (tokenization)

Validated Model Architecture:

• OpenMythos 3B model: 3,041,725,186 parameters (3.04B)
• RDT architecture with Prelude → Recurrent → Coda pipeline
• Support for GQA (Grouped Query Attention) and MLA (Multi-head Latent Attention)
• Configs available: 1B, 3B, 7B, 16B variants

Created Automation Scripts:

1. setup_complete.sh - Master installer for all dependencies
2. setup_azure.sh - Azure CLI authentication helper
3. train_local.sh - Interactive local training menu (3 modes)
4. train.sh - Azure Spot VM orchestration with monitoring
5. All scripts made executable and tested

PENDING:

1. Azure Authentication: User needs to run ./setup_azure.sh for one-time browser login (optional, only needed for Azure Spot VM training)
2. GPU Setup: No local GPU detected; can use Azure Spot VM ($0.15/run) or other cloud providers
3. Minor cosmetic issue: Spectral radius validation test needs tensor reshape (doesn't block training)

CODE STATE:

Fixed Files:

preflight_check.py:

# Line 40 - Fixed class name
class PreflightValidation:
    def __init__(self):
        self.failures = 0
        self.warnings = 0
        self.passes = 0

azure_train_production.py:

# Line 299 - Fixed operator precedence
if result and ("K80" in result or "GPU" in result):
    logger.info(f"✓ GPU ready: {result.strip()}")
    return True

# Lines 438-443 - Added safety checks
if gpu_result:
    parts = gpu_result.strip().split(", ")
    if len(parts) >= 3:
        gpu, mem, total = parts[0], parts[1], parts[2]
        logger.info(f"GPU Util: {gpu}% | Memory: {mem}/{total}MB")
    else:
        logger.warning(f"Unexpected GPU output: {gpu_result.strip()}")

Key Training Scripts:

• train_security_model.py (268 lines) - Small model for security/pentesting data
• training/3b_fine_web_edu.py (552 lines) - Full 3B pretraining with FineWeb-Edu dataset, FSDP support
• azure_train_production.py (501 lines) - Complete Azure orchestration with preflight checks, VM provisioning, monitoring, cleanup
• preflight_check.py (238 lines) - System validation script

OpenMythos Core:

• open_mythos/main.py (44KB, 1089+ lines) - Core RDT implementation
• open_mythos/variants.py (199 lines) - Model configurations (1B, 3B, 7B, 16B)
• open_mythos/tokenizer.py - GPT-OSS-20B tokenizer integration

RELEVANT CODE/DOCUMENTATION SNIPPETS:

Training Command Options:

# Local training (immediate use)
./train_local.sh
  # Option 1: Quick test (100 steps, 2-5 min)
  # Option 2: Security model (5000 steps, 30-60 min)
  # Option 3: Full 3B training (multi-hour)

# Azure training (after setup)
./setup_azure.sh    # One-time login
./train.sh          # Run training ($0.15/run)

# Direct Python
source venv/bin/activate
python3 train_security_model.py --steps 5000
python3 training/3b_fine_web_edu.py

System Configuration:

• Hardware: 16 CPU cores, 18GB RAM (no GPU detected)
• OS: Kali Linux with externally managed Python (resolved via venv)
• Training Paths: Local CPU, Local GPU, Azure Spot VM
• Cost: $0.15/run (Azure) or FREE (local)

Model Variants Config:

def mythos_3b() -> MythosConfig:
    """3B parameter config. Compact inference model."""...

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