SenecaLLM has been trained and fine-tuned for nearly one month—around 100 hours in total—using various systems such as 1x4090, 8x4090, and 3xH100, focusing on the following cybersecurity topics. Its goal is to think like a cybersecurity expert and assist with your questions. It has also been fine-tuned to counteract malicious use.
It does not pursue any profit.
Over time, it will specialize in the following areas:
Incident Response
Threat Hunting
Code Analysis
Exploit Development
Reverse Engineering
Malware Analysis
"Those who shed light on others do not remain in darkness..."
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo AlicanKiraz0/SenecaLLM-Q4_K_M-GGUF --hf-file senecallm-q4_k_m.gguf -p "The meaning to life and the universe is"
