README.md

---
library_name: transformers
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: PrisimAI-chat
  results: []
datasets:
- CJHauser/basic-general-use-dataset
language:
- en
metrics:
- bertscore
---

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# 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 christopher.j.hauser2025@outlook.com .

### 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: christopher.j.hauser2025@outlook.com
Website: https://prisimai.github.io/PrisimAI
## We hope you find CloudGPT useful for your projects! Thank you for supporting open-source AI development.