Update README.md

README.md

@@ -1,51 +1,44 @@
----
-base_model: AlicanKiraz0/SenecaLLM-x-QwQ-32B
-license: apache-2.0
-tags:
-- llama-cpp
-- gguf-my-repo
----
-
-# AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q8_0-GGUF
-This model was converted to GGUF format from [`AlicanKiraz0/SenecaLLM-x-QwQ-32B`](https://huggingface.co/AlicanKiraz0/SenecaLLM-x-QwQ-32B) 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/SenecaLLM-x-QwQ-32B) 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/SenecaLLM-x-QwQ-32B-Q8_0-GGUF --hf-file senecallm-x-qwq-32b-q8_0.gguf -p "The meaning to life and the universe is"
-```
-
-### Server:
-```bash
-llama-server --hf-repo AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q8_0-GGUF --hf-file senecallm-x-qwq-32b-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/SenecaLLM-x-QwQ-32B-Q8_0-GGUF --hf-file senecallm-x-qwq-32b-q8_0.gguf -p "The meaning to life and the universe is"
-```
-or 
-```
-./llama-server --hf-repo AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q8_0-GGUF --hf-file senecallm-x-qwq-32b-q8_0.gguf -c 2048
-```
+---
+license: mit
+base_model:
+- Qwen/QwQ-32B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- cybersecurity
+- ethicalhacking
+- informationsecurity
+- pentest
+- code
+- applicationsecurity
+---
+
+<img src="https://huggingface.co/AlicanKiraz0/SenecaLLM-x-QwQ-32B-Q4_K_M-GGUF/resolve/main/QwQ-32B_x_Seneca_v1.4.png" width="1000" />
+
+Finetuned 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
+
+With the release of the new Qwen QwQ-32B, I quickly began training SenecaLLM v1.4 based on this model. During training:
+* About 30 hours on BF16 with 4×H200
+
+
+**It does not pursue any profit.**
+
+With the new dataset I’ve prepared, it can produce quite good outputs in the following areas:
+* Information Security v1.5
+* Incident Response v1.3.1
+* Threat Hunting v1.3.2
+* Ethical Exploit Development v2.0
+* Purple Team Tactics v1.3
+* Reverse Engineering v2.0
+
+"Those who shed light on others do not remain in darkness..."