Triangle104/SenecaLLM_x_Qwen2.5-7B-CyberSecurity-Q5_K_M-GGUF

This model was converted to GGUF format from AlicanKiraz0/SenecaLLM_x_Qwen2.5-7B-CyberSecurity using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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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.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/SenecaLLM_x_Qwen2.5-7B-CyberSecurity-Q5_K_M-GGUF --hf-file senecallm_x_qwen2.5-7b-cybersecurity-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/SenecaLLM_x_Qwen2.5-7B-CyberSecurity-Q5_K_M-GGUF --hf-file senecallm_x_qwen2.5-7b-cybersecurity-q5_k_m.gguf -c 2048

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 Triangle104/SenecaLLM_x_Qwen2.5-7B-CyberSecurity-Q5_K_M-GGUF --hf-file senecallm_x_qwen2.5-7b-cybersecurity-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/SenecaLLM_x_Qwen2.5-7B-CyberSecurity-Q5_K_M-GGUF --hf-file senecallm_x_qwen2.5-7b-cybersecurity-q5_k_m.gguf -c 2048