# MONIKA Codebase Audit Summary

## Issue Identified

The codebase was silently falling back to a test-only implementation when PyTorch import failed. This was causing MONIKA to generate only canned template responses instead of real language model outputs.

## Changes Made

### 1. Removed Silent Fallback (`salience_os_seed/proto_lm/trainer.py`)
- **Before**: Code caught OSError and silently used fallback mode
- **After**: Code raises `RuntimeError` with clear error message when PyTorch fails to import
- **Impact**: Users will immediately know if PyTorch needs to be fixed, rather than getting unexpected behavior

### 2. Cleaned Up Fallback Module (`salience_os_seed/proto_lm/_fallback.py`)
- Removed unnecessary PyTorch import attempt from fallback
- Added comment clarifying it's for unit tests only
- Fixed `_make_tensor` to not reference removed `_torch` variable

## Current Status

**PyTorch Import Issue**: Your PyTorch installation is failing to load DLLs:
```
OSError: [WinError 127] The specified procedure could not be found. 
Error loading "shm.dll" or one of its dependencies.
```

**Possible Causes**:
1. Python 3.13 compatibility - PyTorch may not fully support Python 3.13 yet
2. Missing Visual C++ Redistributables
3. CUDA driver/runtime mismatch with CUDA 13
4. Corrupted PyTorch installation

## Recommended Fix

Since you mentioned you have CUDA 13 and latest PyTorch working, try:

```bash
# Verify Python version
python --version

# Reinstall PyTorch for CUDA 13
pip uninstall torch torchvision torchaudio
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu131
```

Or if you're using a specific Python version that PyTorch doesn't support, consider using Python 3.11 or 3.12 which have better PyTorch support.

## Architecture Notes

The codebase architecture is sound:
- Proper SASS (Salience-Addressable State Space) implementation
- Clean separation between runtime, training, and conversation components
- Well-structured checkpoint management
- Good vocabulary/tokenization system

Once PyTorch import succeeds, the system should work correctly with the experimental architecture as intended.