Downloads · 30 days
15
20% of all-time downloads
SadokBarbouche/gophos
gophos is a text generation model from SadokBarbouche. Use it when you need the model to write or continue text. It is set up for transformers.
This repository contains a fine-tuned version of the Gemma 2B-IT model, tailored specifically for interpreting Sophos logs exported from Splunk. The model is hosted on Hugging Face for easy integration and usage in va…
Downloads · 30 days
15
20% of all-time downloads
All-time downloads
74
Public
Parameters
2.5B
5 GB on disk
Likes
0
Public
Click a slice to open those files.
.safetensors5 GB · 100%
From the Hugging Face model README
This repository contains a fine-tuned version of the Gemma 2B-IT model, tailored specifically for interpreting Sophos logs exported from Splunk. The model is hosted on Hugging Face for easy integration and usage in various applications requiring interpretation and analysis of Sophos logs.
The Gemma 2B-IT model, has been fine-tuned using a dataset of Sophos logs extracted from Splunk. Through this fine-tuning process, the model has been optimized to effectively interpret and extract meaningful information from Sophos logs, facilitating tasks such as threat detection, security analysis, and incident response.
To utilize the model, simply install the Hugging Face transformers library and load the model using its unique identifier or name:
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# Load the fine-tuned Gemma 2B-IT model
model = AutoModelForSequenceClassification.from_pretrained("SadokBarbouche/gophos")
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("SadokBarbouche/gophos")
The fine-tuning of the Gemma 2B-IT model was conducted using a dataset of Sophos logs exported from Splunk. The dataset was preprocessed to ensure compatibility with the model architecture and to optimize training performance.
We would like to acknowledge the creators of the Gemma 2B-IT model for their pioneering work in natural language understanding. Additionally, we extend our gratitude to the contributors of the Hugging Face transformers library for their valuable tools and resources.