README.md

---
license: mit
base_model: AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2
pipeline_tag: text-classification
tags:
- gguf-my-repo
- llama-cpp
- qwen2.5
- pentest
- ethical-hacking
- informationsecurity
---


<img src="https://huggingface.co/AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2-Q8_0-GGUF/resolve/main/Ekran%20Resmi%202025-01-06%2018.31.40.png" width="1000" />

Curated and trained by Alican Kiraz

[![Linkedin](https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white)](https://tr.linkedin.com/in/alican-kiraz) 
![X (formerly Twitter) URL](https://img.shields.io/twitter/url?url=https%3A%2F%2Fx.com%2FAlicanKiraz0) 
![YouTube Channel Subscribers](https://img.shields.io/youtube/channel/subscribers/UCEAiUT9FMFemDtcKo9G9nUQ) 

Links:
- Medium: https://alican-kiraz1.medium.com/
- Linkedin: https://tr.linkedin.com/in/alican-kiraz
- X: https://x.com/AlicanKiraz0
- YouTube: https://youtube.com/@alicankiraz0

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..."


# AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2-Q8_0-GGUF
This model was converted to GGUF format from [`AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2`](https://huggingface.co/AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2) for more details on the model.

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

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2-Q8_0-GGUF --hf-file qwq-32b-preview-senecallmv1.2-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2-Q8_0-GGUF --hf-file qwq-32b-preview-senecallmv1.2-q8_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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/QwQ-32B-Preview-SenecaLLMv1.2-Q8_0-GGUF --hf-file qwq-32b-preview-senecallmv1.2-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo AlicanKiraz0/QwQ-32B-Preview-SenecaLLMv1.2-Q8_0-GGUF --hf-file qwq-32b-preview-senecallmv1.2-q8_0.gguf -c 2048
```