metadata

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: apache-2.0
language:
  - en

NeXGen - A Text Generative Model

Note- this is the large version of NeXGen series we,ll realise larger versions of NeXGen soon stay-tuned.

Based version of NeXGen at: CrabfishAI/NeXGen-based

Small version of NeXGen at: CrabfishAI/NeXGen-small

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:

from transformers import AutoTokenizer, AutoModelForCausalLM

# Specify the model name from Hugging Face Model Hub
model_name = "CrabfishAI/NeXGen-large"
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

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.
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.
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.
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.