CloudGPT

Overview

CloudGPT is an advanced AI language model developed by PrisimAI , based on the architecture of GPT-2 . This model is fine-tuned to handle a variety of natural language tasks, including text generation, summarization, question-answering, and more. With its robust training and optimization, CloudGPT is designed to deliver high-quality outputs while maintaining flexibility for diverse use cases.

This repository contains the model weights and instructions for using CloudGPT. Whether you're a researcher, developer, or enthusiast, this model provides a powerful tool for exploring the capabilities of large language models.

Model Details

Base Architecture

Base Model : GPT-2 Model Type : Transformer-based autoregressive language model Parameters : ~1.5B (based on GPT-2 Large)

Training Data

Pre-training : The model was initially pre-trained on the extensive OpenWebText dataset, ensuring a strong foundation in general language understanding. Fine-tuning : Additional fine-tuning was performed on a proprietary dataset curated by PrisimAI , focusing on enhancing conversational abilities, factual accuracy, and contextual awareness.

Key Features

Versatile Text Generation : Capable of generating coherent and contextually relevant text across various domains. Improved Context Handling : Enhanced ability to maintain context over longer conversations or documents. Customizable Outputs : Supports temperature, top-k, and top-p sampling for controlling creativity and output diversity.

Usage

Installation

To use CloudGPT, ensure you have the transformers library installed:

    bash
    pip install transformers

Loading the Model

You can load CloudGPT directly from the Hugging Face Hub using the following code:

    python



    from transformers import AutoTokenizer, AutoModelForCausalLM

    # Load the tokenizer and model
    tokenizer = AutoTokenizer.from_pretrained("prisimai/CloudGPT")
    model = AutoModelForCausalLM.from_pretrained("prisimai/CloudGPT")

    # Example input
    input_text = "Once upon a time"
    input_ids = tokenizer.encode(input_text, return_tensors="pt")

    # Generate text
    output = model.generate(input_ids, max_length=50, num_return_sequences=1)
    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)

    print(generated_text)
    Parameters for Text Generation

You can customize the text generation process by adjusting the following parameters:

    max_length: Maximum length of the generated text.
    temperature: Controls randomness (lower values make outputs more deterministic).
    top_k: Limits the sampling pool to the top-k highest probability tokens.
    top_p: Implements nucleus sampling by considering only tokens with cumulative probability up to top_p.

Example:

    python

    output = model.generate(
    input_ids,
    max_length=100,
    temperature=0.7,
    top_k=50,
    top_p=0.95,
    num_return_sequences=1
    )

Limitations

While CloudGPT is a powerful language model, it has certain limitations:

Bias : Like most large language models, CloudGPT may inadvertently generate biased or inappropriate content due to biases in the training data.

Factuality : Although fine-tuned for improved factual accuracy, the model may occasionally produce incorrect or misleading information.

Context Length : The maximum context length is limited by the underlying GPT-2 architecture (~1024 tokens).

Users are encouraged to implement safeguards and post-processing steps when deploying this model in real-world applications.

Ethical Considerations

PrisimAI is committed to promoting responsible AI usage. We recommend the following practices when working with CloudGPT:

Bias Mitigation : Regularly audit outputs for potential biases and take corrective actions.

Transparency : Clearly disclose when content is generated by an AI model.

Safety Filters : Implement filters to prevent harmful or inappropriate content from being generated.

If you encounter any ethical concerns or issues while using this model, please report them to us at [email protected] .

Citation

If you use CloudGPT in your research or projects, please cite it as follows:

    @misc{cloudgpt2023,
      title={CloudGPT: A Fine-Tuned GPT-2 Language Model by PrisimAI},
      author={PrisimAI},
      year={2023},
      publisher={Hugging Face},
      url={https://huggingface.co/prisimai/CloudGPT }
    }

License

CloudGPT is released under the MIT License . By using this model, you agree to abide by the terms of the license. See the LICENSE file for more details.

Contact

For inquiries, feedback, or collaboration opportunities, please reach out to us at:

Email: [email protected] Website: https://prisimai.github.io/PrisimAI

PrisimAI
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CloudGPT