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feat: initial nyx-probing framework structure

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- Basic package structure (core, probes, analysis, storage, cli)
- probe_test.py - minimal probe that works on THE SPINE
- First successful probe: 2025-12-06
- Apache 2.0 license
- Qwen2.5-7B-Base on RTX 3090 (14.2GB VRAM)

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
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dafit
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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
venv/
ENV/
.venv/
# IDE
.idea/
.vscode/
*.swp
*.swo
# Jupyter
.ipynb_checkpoints/
# Results (large files)
results/*.json
results/*.csv
*.pt
*.bin
# Environment
.env
.env.local
# OS
.DS_Store
Thumbs.db

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# nyx-probing
Probing framework for understanding Qwen2.5-7B-Base before curriculum design.
## Overview
Part of the [nimmerverse](https://git.eachpath.com/nimmerverse) project. This framework implements the probing protocols defined in [nimmerversity.md](https://git.eachpath.com/nimmerverse/nimmerverse-sensory-network/src/branch/main/nimmerversity.md).
## Probing Capabilities
- **Surface Probe** - Feed single words, capture raw completions
- **Echo Probe** - Depth measurement via completion echo
- **Multilingual Probe** - Cross-language triangulation (EN/DE/AR/ZH)
- **Activation Probe** - Hidden state analysis
## Hardware
- **THE SPINE**: Prometheus with RTX 3090 (24GB VRAM)
- **Model**: Qwen2.5-7B-Base (~14GB loaded)
- **RAM**: 64GB for activation capture
## Quick Start
```bash
# On Prometheus
cd ~/nimmerverse/nyx-probing
source venv/bin/activate
python probe_test.py
```
## Status
Active development. First successful probe: 2025-12-06.
## License
Apache 2.0 - See [LICENSE](LICENSE)

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"""
Minimal probe test for Qwen2.5-7B-Base on THE SPINE.
First successful run: 2025-12-06
"""
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B")
print("Loading model to GPU...")
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2.5-7B",
torch_dtype=torch.float16,
device_map="cuda"
)
print("\n🌙 First probe: 'heartbeat'")
inputs = tokenizer("heartbeat", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=30, do_sample=True, temperature=0.8)
print(tokenizer.decode(outputs[0]))
print("\n🌙 Second probe: 'consciousness'")
inputs = tokenizer("consciousness", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=30, do_sample=True, temperature=0.8)
print(tokenizer.decode(outputs[0]))
print(f"\n📊 GPU Memory Used: {torch.cuda.memory_allocated() / 1024**3:.2f} GB")
print(f"📊 GPU Memory Reserved: {torch.cuda.memory_reserved() / 1024**3:.2f} GB")
print("\n✅ THE SPINE IS THINKING!")

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# Core ML
torch>=2.1.0
transformers>=4.36.0
accelerate>=0.25.0
# Data handling
numpy>=1.24.0
pandas>=2.0.0
# CLI
click>=8.1.0
rich>=13.0.0
# Configuration
pydantic>=2.5.0
pyyaml>=6.0.0
python-dotenv>=1.0.0
# Development
jupyter>=1.0.0
matplotlib>=3.8.0