neuraldebug（neuraldebug 搜索）

NeuralDebug

AI-powered debugging framework for software and LLM reasoning. Part of the DeepRhapsody project.

Use this skill when asked to debug a program, diagnose a crash, analyze a core dump, inspect LLM reasoning, detect hallucinations, or fine-tune a model.

What NeuralDebug Does

🔧 Software Debugging (8 Languages)

Debug Python, C/C++, C#, Rust, Java, Go, Node.js/TypeScript, and Ruby using real debuggers — not code reading. NeuralDebug drives GDB, LLDB, CDB, JDB, Delve, Node Inspector, and rdbg via a unified natural-language interface.

🧠 LLM Debugging

Step through transformer forward passes layer by layer. Run interpretability techniques to understand *why* a model produces a given output: Logit Lens, Attention Analysis, Probing, Activation Patching, and custom analysis sandboxes.

🎯 LLM Fine-Tuning

Inject missing knowledge into GPT-2 family models using LoRA. Diagnose → fine-tune → verify in a single workflow.

Installation

# Clone the repo
git clone https://github.com/DennySun2020/DeepRhapsody.git
cd DeepRhapsody

# Install Python dependencies
pip install torch transformers

# For fine-tuning (optional)
pip install peft==0.7.1

Quick Start: Software Debugging

Interactive Mode (persistent debug session)

# Start debug server for any supported language
python src/NeuralDebug/python_debug_session.py serve --port 5678

# Send commands via natural language
python src/NeuralDebug/python_debug_session.py cmd -p 5678 launch my_script.py
python src/NeuralDebug/python_debug_session.py cmd -p 5678 set_breakpoint 42
python src/NeuralDebug/python_debug_session.py cmd -p 5678 continue
python src/NeuralDebug/python_debug_session.py cmd -p 5678 inspect

One-Shot Mode (quick breakpoint capture)

python src/NeuralDebug/python_debugger.py debug my_script.py --breakpoint 42 --output result.json

Supported Languages

All scripts live in src/NeuralDebug/ and share the same command interface.

Quick Start: LLM Debugging

# Start LLM debug server
python src/NeuralDebug/llm/llm_debug_session.py serve -m gpt2-medium -p 5680

# Ask the model a question
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 start "The capital of Japan is"
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 generate 20

# Interpretability: where does the answer emerge?
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 logit_lens

# Interpretability: which attention heads focus on "Japan"?
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 attention 3

# Interpretability: what knowledge is encoded per layer?
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 probe next_token

# Interpretability: is prediction Japan-specific?
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 patch "The capital of France is"

LLM Models Supported

Any HuggingFace causal LM with a built-in adapter:

• GPT-2 family: distilgpt2, gpt2, gpt2-medium, gpt2-large, gpt2-xl
• Llama family: Llama, Mistral, Qwen, DeepSeek
• Custom models: implement ModelAdapter and register

Quick Start: LLM Fine-Tuning

# Create a config file (JSON)
cat > ft_config.json << 'EOF'
{
  "facts": [
    "Dr. Elena Vasquez is the director of Horizon Research Labs",
    "Dr. Elena Vasquez leads Horizon Research Labs"
  ],
  "verification_prompt": "Dr. Elena Vasquez is the director of",
  "expected_token": "Horizon",
  "config": { "num_steps": 150, "lora_r": 16, "lora_alpha": 32, "learning_rate": 2e-4 }
}
EOF

# Run fine-tuning (uses same server as LLM debugger)
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 -t 600 finetune ft_config.json

# Verify
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 start "Dr. Elena Vasquez is the director of"
python src/NeuralDebug/llm/llm_debug_session.py cmd -p 5680 generate 20

Architecture

NeuralDebug uses a client-server architecture over TCP/JSON:

AI Agent (OpenClaw, Copilot, Claude, etc.)
    │
    ▼
Debug Session Script (TCP client)
    │
    ▼
NeuralDebug Server (TCP server on configurable port)
    │
    ▼
Real Debugger Backend (GDB/LLDB/CDB/PyTorch hooks/etc.)

Every command returns structured JSON — parseable by any AI agent.

Platform Support

• Windows (CDB, Visual Studio debugger)
• Linux (GDB, LLDB)
• macOS (LLDB, GDB)

Links

• Repository: https://github.com/DennySun2020/DeepRhapsody
• Documentation: https://github.com/DennySun2020/DeepRhapsody/wiki
• Issues: https://github.com/DennySun2020/DeepRhapsody/issues

See the references/ folder for detailed command documentation:

• software-debugging.md — full command reference for all 8 languages
• llm-debugging.md — interpretability techniques and LLM commands
• llm-finetuning.md — LoRA fine-tuning workflow and configuration