Instructions to use RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf with Ollama:
ollama run hf.co/RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M
- Unsloth Studio
How to use RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/diabolic6045_-_harry_potter_chatbot-gguf:Q4_K_M
Run and chat with the model
lemonade run user.diabolic6045_-_harry_potter_chatbot-gguf-Q4_K_M
List all available models
lemonade list
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
harry_potter_chatbot - GGUF
- Model creator: https://huggingface.co/diabolic6045/
- Original model: https://huggingface.co/diabolic6045/harry_potter_chatbot/
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!
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