---
tags:
- autotrain
- text-generation
widget:
- text: Once upon a time,
- text: My name is john and my hobby is
- text: My hobby was playing cricket but now i
- text: I asked my biology teacher that
- text: I love playing
- text: I came back to home to pet my cat but then
- text: I never received a letter from John Lewis after he
license: mit
language:
- en
---




# NeXGen - A Text Generative Model
Note- this is the smallest version of NeXGen series we,ll realise larger versions of NeXGen soon stay-tuned.

Based version of NeXGen at: [CrabfishAI/NeXGen-based](https://huggingface.co/CrabfishAI/NeXGen-based) 

Large version of NexGen at: [CrabfishAI/NeXGen-large](https://huggingface.co/CrabfishAI/NeXGen-large)

Introduction-NeXGen is a state-of-the-art text generative model designed to meet diverse needs, from creative writing to content creation. This model leverages advanced natural language processing techniques to provide human-like text generation with a wide range of applications.

## Features

- **Creative Content Generation:** NeXGen excels at generating creative writing, including stories, poetry, and fictional narratives.
- **Contextual Awareness:** The model understands context, ensuring coherent and contextually appropriate responses.
- **User-Friendly Interface:** NeXGen offers an intuitive and user-friendly interface for seamless integration into various applications.
- **Versatility:** From content creation to educational support, NeXGen adapts to different writing styles and applications.
- **Advanced Architecture:** Built on the latest advancements in natural language processing, NeXGen offers high-quality text generation.

## Uses

NeXGen finds application in various domains, including:

- **Content Creation:** Generate marketing copy, stories, and product descriptions.
- **Assistance in Writing:** Aid authors, bloggers, and students in drafting articles and essays.
- **Chatbot Development:** Power conversational agents with human-like responses.
- **Prototyping and Idea Generation:** Facilitate brainstorming sessions for product development.
- **Social Media Content:** Generate engaging captions for social media posts.
- **Personal Assistant Applications:** Assist users in drafting emails and messages.

## Direct Use Cases

NeXGen can be directly employed for:

- **Automated Email Drafting:** Quickly compose emails with NeXGen's assistance.
- **Blog Post Generation:** Generate sections or entire articles based on a given topic.
- **Code Commenting:** Improve code documentation with clear and concise comments.
- **Storyline Creation for Games:** Create dynamic and engaging storylines for video games.
- **Learning Material Generation:** Develop study guides and educational content.
- **Personal Journaling Assistance:** Receive prompts and suggestions for journaling.

## Getting Started

To download NeXGen use this code:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Specify the model name from Hugging Face Model Hub
model_name = "CrabfishAI/NeXGen-small"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

def generate_text(prompt, max_length=100, num_beams=5, no_repeat_ngram_size=2, top_k=50, top_p=0.95, temperature=0.7):
    input_ids = tokenizer.encode(prompt, return_tensors="pt")

    # Ensure attention_mask is provided
    attention_mask = input_ids.ne(tokenizer.pad_token_id).float()

    # Generate output text
    output = model.generate(
        input_ids,
        max_length=max_length,
        num_beams=num_beams,
        no_repeat_ngram_size=no_repeat_ngram_size,
        top_k=top_k,
        top_p=top_p,
        temperature=temperature,
        attention_mask=attention_mask  # Pass attention_mask to the generation method
    )

    decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
    return decoded_output

# Example usage:
prompt = "Your prompt here"
generated_text = generate_text(prompt, max_length=200)

print("Generated Text:")
print(generated_text)
```

## Limitation

1. **Content Quality**: The model's output may vary in quality, and there's a possibility it might generate content that is nonsensical, irrelevant, or grammatically incorrect.

2. **Bias and Sensitivity**: The model is trained on diverse data, but it may inadvertently exhibit biases or generate content that is sensitive or inappropriate. Exercise caution and review generated text before use.

3. **Inappropriate Language**: The model might generate text that includes offensive language or inappropriate content. Be mindful of this, especially in applications where maintaining a respectful and inclusive tone is essential.

4. **Ambiguous Prompts**: The quality of generated text is highly dependent on the prompt provided. Ambiguous or unclear prompts may result in less coherent or relevant outputs.

## Disclaimer

- **Use with Caution**: This model is a tool that should be used with caution. Always review and validate the generated text before incorporating it into any application or publication.

- **Not for Critical Applications**: Avoid using the model for critical applications where accuracy and reliability are paramount. The model is intended for creative and exploratory purposes.

- **Ongoing Improvement**: The model may be updated or fine-tuned for better performance. Stay informed about updates and consider using the latest version for improved results.