Update README.md
Browse files# Dimeltech/Tenezis-8B-Instruct
## Overview
**Model Name**: Tenezis-8B-Instruct
**Organization**:[Dimel Tech](https://huggingface.co/Dimeltech) (African-Cameroonian startup specializing in artificial intelligence and cybersecurity)
**Application Domains**: Artificial Intelligence, Cybersecurity, Natural Language Processing
## Description
Tenezis-8B-Instruct is a natural language processing model developed by Dimel Tech. This model is designed to understand and generate complex instructions in natural language, facilitating various applications in AI and cybersecurity.
## Features
- **Model Size**: 8 billion parameters
- **Architecture**: Based on the GPT-3.5 architecture
- **Supported Languages**: Multilingual with a particular focus on African and international languages
- **Training**: Trained on a large corpus of multilingual and multi-domain data
- **Capabilities**:
- Text understanding and generation
- Assistance for writing and text correction
- Applications in cybersecurity, such as threat detection and security report generation
## Use Cases
- **Virtual Assistants**: Enhancing user interactions with intelligent virtual assistants.
- **Text Analysis**: Information extraction and semantic analysis of large documents.
- **Cybersecurity**: Generating security reports, detecting anomalies, and vulnerability analysis.
- **Customer Support**: Automating and improving responses to customer inquiries.
## Performance
The Tenezis-8B-Instruct model has demonstrated robust performance in various natural language processing tasks, including coherent text generation and deep contextual understanding. It is particularly effective in contexts requiring precise analysis and multilingual text generation.
## Limitations
- **Bias**: Like all large language models, Tenezis-8B-Instruct may reflect biases present in the training data.
- **Contextual Understanding**: While performant, the model can sometimes struggle with extremely complex or ambiguous contexts.
- **Security**: Users should be aware of the security and privacy limitations when using the model in sensitive applications.
## Ethics and Impact
Dimel Tech is committed to developing ethical and responsible technologies. The development and deployment of Tenezis-8B-Instruct follow strict guidelines to minimize biases and maximize positive impact.
## How to Use
You can access and use the Tenezis-8B-Instruct model via the Hugging Face API. Here is an example code to get started:
```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model_name = "Dimeltech/Tenezis-8B-Instruct"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
input_text = "Your text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs.input_ids, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)