eBPF-LLM-NetSentinel/.env.example at main · NagatoBigSeven/eBPF-LLM-NetSentinel · GitHub

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# eBPF-LLM NetSentinel Environment Variables
# Copy this file to .env and fill in your configuration

# ==================== LLM Backend Selection ====================
# Options: zhipuai (cloud, default), ollama (local service), huggingface (local model)
LLM_BACKEND=zhipuai

# ==================== Zhipu AI Configuration (Cloud) ====================
ZHIPUAI_API_KEY=your_api_key_here
ZHIPUAI_BASE_URL=https://open.bigmodel.cn/api/paas/v4/
ZHIPUAI_MODEL=glm-4-flash

# ==================== Ollama Configuration (Local Service) ====================
OLLAMA_HOST=localhost
OLLAMA_PORT=11434
OLLAMA_MODEL=qwen3:8b

# ==================== HuggingFace Configuration (Local Model) ====================
# Model ID (HuggingFace Hub) or local path
HF_MODEL=Qwen/Qwen2.5-1.5B-Instruct
# HF_MODEL=/path/to/local/model
HF_DEVICE=auto   # cuda, cpu, auto
HF_QUANTIZE=none # 4bit, 8bit, none (quantization requires bitsandbytes)

# ==================== Usage Examples ====================
#
# Using Zhipu AI (default):
#   python3 llm_analyzer.py
#
# Using Ollama:
#   python3 llm_analyzer.py --backend ollama
#
# Using HuggingFace (auto-download):
#   python3 llm_analyzer.py --backend huggingface --hf-model Qwen/Qwen2.5-1.5B-Instruct
#
# Using local model path:
#   python3 llm_analyzer.py --backend huggingface --hf-model /path/to/model
#
# Using 4-bit quantization to save VRAM:
#   pip install bitsandbytes
#   python3 llm_analyzer.py --backend huggingface --hf-quantize 4bit