transformers generated text-generation conversational pytorch ShareAI Felguk license:apache-2.0 endpoints_compatible region:us
---
license: apache-2.0
tags:
- generated
- text-generation
- conversational
- pytorch
- transformers
- ShareAI
- Felguk
---

# <img src="https://huggingface.co/shareAI/Felguk0.5-turbo-preview/resolve/main/hd_e8ecc8aad81eb559a52d229a8d7b0d8a_677b9eaf4d161.png" alt="Felguk0.5-turbo-preview" width="500"/>

[![Model License](https://img.shields.io/badge/license-Apache%202.0-blue)](LICENSE)
[![Hugging Face Model](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model%20Hub-orange)](https://huggingface.co/shareAI/Felguk0.5-turbo-preview)
[![Transformers Documentation](https://img.shields.io/badge/📖-Transformers%20Docs-blueviolet)](https://huggingface.co/docs/transformers/index)

The **Felguk0.5-turbo-preview** model is a preview version of a powerful language model developed by ShareAI. It is designed for text generation, conversational systems, and other NLP tasks. Built on the Transformer architecture, this model is optimized for high performance.

## All Felguk Models on Hugging Face

Here’s a list of all available models under the `felguk` namespace on Hugging Face:

| Model Name                          | Description                                                                 | Link                                                                 |
|-------------------------------------|-----------------------------------------------------------------------------|----------------------------------------------------------------------|
| `shareAI/Felguk0.5-turbo-preview`   | A preview version of the Felguk model for text generation and conversation. | [Model Page](https://huggingface.co/shareAI/Felguk0.5-turbo-preview) |
| `shareAI/Felguk0.5-base`            | The base version of the Felguk model for general-purpose NLP tasks.         | [Model Page](https://huggingface.co/shareAI/Felguk0.5-base)          |
| `shareAI/Felguk0.5-large`           | A larger version of the Felguk model with enhanced capabilities.            | [Model Page](https://huggingface.co/shareAI/Felguk0.5-large)         |
| `shareAI/Felguk0.5-multilingual`    | A multilingual variant of the Felguk model for cross-language tasks.        | [Model Page](https://huggingface.co/shareAI/Felguk0.5-multilingual)  |

> **Note:** Currently, only the **Felguk0.5-turbo-preview** model is available. The other models listed above are planned for future release and are not yet accessible.

> **Future Plans:** We are excited to announce that **Felguk v1** is in development! This next-generation model will feature improved performance, enhanced multilingual support, and new capabilities for advanced NLP tasks. Stay tuned for updates!

## What Can It Do? 🚀

The **Felguk0.5-turbo-preview** model is a versatile tool for a wide range of NLP tasks. Here’s what it can do:

- **📝 Text Generation**: Create high-quality text for stories, articles, or creative writing.
- **💬 Conversational AI**: Power chatbots and virtual assistants with natural, human-like responses.
- **🌐 Multilingual Support**: Handle multiple languages for global applications (coming soon in future versions).
- **🔍 Summarization**: Generate concise summaries of long documents or articles.
- **❓ Question Answering**: Provide accurate answers to user queries based on context.
- **🧠 Knowledge Integration**: Leverage pre-trained knowledge for informed and context-aware responses.

## Usage

To use the model with the `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "shareAI/Felguk0.5-turbo-preview"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example input
input_text = "Hello! How are you?"

# Tokenize and generate a response
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)

# Decode and print the result
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)