ffe47c51b5
Full security platform with web dashboard, 16 Flask blueprints, 26 modules, autonomous AI agent, WebUSB hardware support, and Archon Android companion app. Includes Hash Toolkit, debug console, anti-stalkerware shield, Metasploit/RouterSploit integration, WireGuard VPN, OSINT reconnaissance, and multi-backend LLM support. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
47 lines
1.3 KiB
Plaintext
47 lines
1.3 KiB
Plaintext
# AUTARCH LLM Configuration Template
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# Hardware: Orange Pi 5 Plus (RK3588 SoC, 8-core ARM, 16GB RAM)
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# Optimized for: CPU-only inference on ARM64
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#
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# This configuration is optimized for the Orange Pi 5 Plus running
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# CPU-only inference. The RK3588 has 4x Cortex-A76 + 4x Cortex-A55 cores.
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# Best with quantized GGUF models (Q4_K_M or Q5_K_M).
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[llama]
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# GGUF Model Settings (llama.cpp)
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# Recommended: Use Q4_K_M or Q5_K_M quantized models
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model_path =
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n_ctx = 2048
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n_threads = 4
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n_gpu_layers = 0
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temperature = 0.7
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top_p = 0.9
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top_k = 40
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repeat_penalty = 1.1
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max_tokens = 1024
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seed = -1
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[transformers]
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# SafeTensors Model Settings (HuggingFace)
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# Note: CPU inference is slow with transformers, GGUF recommended
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model_path =
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device = cpu
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torch_dtype = float32
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load_in_8bit = false
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load_in_4bit = false
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trust_remote_code = false
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max_tokens = 1024
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temperature = 0.7
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top_p = 0.9
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top_k = 40
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repetition_penalty = 1.1
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# Notes:
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# - n_threads = 4 uses only the fast A76 cores (better perf than all 8)
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# - n_ctx = 2048 balances memory usage and capability
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# - n_gpu_layers = 0 for pure CPU inference
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# - Strongly recommend GGUF Q4_K_M models for best speed
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# - 7B Q4 models use ~4GB RAM, leaving room for system
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# - max_tokens = 1024 keeps generation times reasonable
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# - For transformers: CPU with float32 is slow but works
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# - Avoid 13B+ models unless heavily quantized
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