Quantization made by Richard Erkhov.

harry_potter_chatbot - GGUF

Model creator: https://huggingface.co/diabolic6045/
Original model: https://huggingface.co/diabolic6045/harry_potter_chatbot/

Name	Quant method	Size
harry_potter_chatbot.Q2_K.gguf	Q2_K	0.17GB
harry_potter_chatbot.IQ3_XS.gguf	IQ3_XS	0.18GB
harry_potter_chatbot.IQ3_S.gguf	IQ3_S	0.19GB
harry_potter_chatbot.Q3_K_S.gguf	Q3_K_S	0.19GB
harry_potter_chatbot.IQ3_M.gguf	IQ3_M	0.2GB
harry_potter_chatbot.Q3_K.gguf	Q3_K	0.21GB
harry_potter_chatbot.Q3_K_M.gguf	Q3_K_M	0.21GB
harry_potter_chatbot.Q3_K_L.gguf	Q3_K_L	0.23GB
harry_potter_chatbot.IQ4_XS.gguf	IQ4_XS	0.22GB
harry_potter_chatbot.Q4_0.gguf	Q4_0	0.23GB
harry_potter_chatbot.IQ4_NL.gguf	IQ4_NL	0.23GB
harry_potter_chatbot.Q4_K_S.gguf	Q4_K_S	0.23GB
harry_potter_chatbot.Q4_K.gguf	Q4_K	0.25GB
harry_potter_chatbot.Q4_K_M.gguf	Q4_K_M	0.25GB
harry_potter_chatbot.Q4_1.gguf	Q4_1	0.25GB
harry_potter_chatbot.Q5_0.gguf	Q5_0	0.27GB
harry_potter_chatbot.Q5_K_S.gguf	Q5_K_S	0.27GB
harry_potter_chatbot.Q5_K.gguf	Q5_K	0.29GB
harry_potter_chatbot.Q5_K_M.gguf	Q5_K_M	0.29GB
harry_potter_chatbot.Q5_1.gguf	Q5_1	0.29GB
harry_potter_chatbot.Q6_K.gguf	Q6_K	0.32GB
harry_potter_chatbot.Q8_0.gguf	Q8_0	0.41GB

Original model description:

Harry Potter Chatbot

This model is a chatbot designed to generate responses in the style of Harry Potter, the protagonist from J.K. Rowling's popular book series and its movie adaptations.

Model Architecture

The harry_potter_chatbot is based on the DialoGPT-medium model, a powerful GPT-based architecture designed for generating conversational responses. It has been fine-tuned on a dataset of Harry Potter's dialogues from movie transcripts.

Usage

You can use this model to generate responses for a given input text using the following code:

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("diabolic6045/harry_potter_chatbot")
model = AutoModelForCausalLM.from_pretrained("diabolic6045/harry_potter_chatbot")

input_text = "What's your favorite spell?"
input_tokens = tokenizer.encode(input_text, return_tensors='pt')
output_tokens = model.generate(input_tokens, max_length=50, num_return_sequences=1)
output_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)

print(output_text)

Limitations

This model is specifically designed to generate responses in the style of Harry Potter and may not provide accurate or coherent answers to general knowledge questions. It may also sometimes generate inappropriate responses. Be cautious while using this model in a public setting or for critical applications.

Training Data

The model was fine-tuned on a dataset of Harry Potter's dialogues from movie transcripts. The dataset was collected from publicly available movie scripts and includes conversations and quotes from various Harry Potter films.

Acknowledgments

This model was trained using the Hugging Face Transformers library, and it is based on the DialoGPT-medium model by Microsoft. Special thanks to the Hugging Face team and Microsoft for their contributions to the NLP community.

Feel free to test the model and provide feedback or report any issues. Enjoy chatting with Harry Potter!