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: AMD Radeon RX 6700 XT (12GB VRAM)
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# Optimized for: GPU inference with ROCm/HIP support
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#
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# This configuration is optimized for AMD GPUs using ROCm.
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# The RX 6700 XT has 12GB VRAM, excellent for 7B-13B models.
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# Requires ROCm drivers and PyTorch with ROCm support.
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[llama]
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# GGUF Model Settings (llama.cpp)
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# Note: llama.cpp requires HIP/ROCm build for AMD GPU support
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# Build with: CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
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model_path =
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n_ctx = 8192
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n_threads = 8
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n_gpu_layers = -1
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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 = 4096
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seed = -1
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[transformers]
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# SafeTensors Model Settings (HuggingFace)
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# ROCm uses 'cuda' device identifier in PyTorch
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model_path =
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device = cuda
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torch_dtype = float16
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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 = 4096
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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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# - 12GB VRAM allows running 13B models at float16
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# - For 33B+ models, enable load_in_4bit = true
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# - ROCm support requires specific PyTorch version:
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# pip install torch --index-url https://download.pytorch.org/whl/rocm5.6
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# - llama.cpp needs HIP build for GPU acceleration
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# - If GPU not detected, falls back to CPU (check ROCm installation)
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# - n_ctx = 8192 works well with 12GB VRAM
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