Instructions to use Covvenheimer/taylor_swift_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Covvenheimer/taylor_swift_model with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Covvenheimer/taylor_swift_model", filename="unsloth.Q4_K_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Covvenheimer/taylor_swift_model 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 Covvenheimer/taylor_swift_model:Q4_K_M # Run inference directly in the terminal: llama cli -hf Covvenheimer/taylor_swift_model:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Covvenheimer/taylor_swift_model:Q4_K_M # Run inference directly in the terminal: llama cli -hf Covvenheimer/taylor_swift_model: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 Covvenheimer/taylor_swift_model:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Covvenheimer/taylor_swift_model: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 Covvenheimer/taylor_swift_model:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Covvenheimer/taylor_swift_model:Q4_K_M
Use Docker
docker model run hf.co/Covvenheimer/taylor_swift_model:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Covvenheimer/taylor_swift_model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Covvenheimer/taylor_swift_model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Covvenheimer/taylor_swift_model", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Covvenheimer/taylor_swift_model:Q4_K_M
- Ollama
How to use Covvenheimer/taylor_swift_model with Ollama:
ollama run hf.co/Covvenheimer/taylor_swift_model:Q4_K_M
- Unsloth Studio
How to use Covvenheimer/taylor_swift_model 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 Covvenheimer/taylor_swift_model 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 Covvenheimer/taylor_swift_model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Covvenheimer/taylor_swift_model to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Covvenheimer/taylor_swift_model with Docker Model Runner:
docker model run hf.co/Covvenheimer/taylor_swift_model:Q4_K_M
- Lemonade
How to use Covvenheimer/taylor_swift_model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Covvenheimer/taylor_swift_model:Q4_K_M
Run and chat with the model
lemonade run user.taylor_swift_model-Q4_K_M
List all available models
lemonade list
Taylor Swift Lyrics Llama Model (3.2, GGUF Format)
- Base Model: unsloth/llama-3.2-1b-bnb-4bit
- Fine-tuned on: Taylor Swift lyrics using QLoRA
- Format: GGUF (Not compatible with the
transformerslibrary; requiresllama-cpp-python) - License: Apache-2.0
- Developed by: Covvenheimer and Team
This model, fine-tuned on Taylor Swift lyrics, is tailored for generating text in the style of her songs. It was trained with a 2x speed improvement using Unsloth and Hugging Face’s TRL library, specifically optimized for GGUF format.
Important: This model requires
llama-cpp-pythonto run. It is incompatible with thetransformerslibrary due to its GGUF format.
Installation and Setup
To load and run this model, install the llama-cpp-python library and download the model file from the Hugging Face Hub.
Step-by-Step Code Example
Install llama-cpp-python
!pip install llama-cpp-pythonLoad the Model with llama-cpp
from huggingface_hub import hf_hub_download from llama_cpp import Llama # Define your model repository and file name repo_id = "Covvenheimer/taylor_swift_model" filename = "unsloth.Q4_K_M.gguf" # Download the GGUF model file from Hugging Face model_path = hf_hub_download(repo_id=repo_id, filename=filename) # Load the model using llama-cpp-python llm = Llama(model_path=model_path)Generate Text Using a Prompt
# Define a prompt for generating lyrics prompt = """You are a songwriter composing a song in the style of Taylor Swift. Write lyrics that reflect her themes and musical style, focusing on Love, Heartbreak, Romance.""" # Generate lyrics output = llm(prompt, max_tokens=512, temperature=0.8) print(output["choices"][0]["text"])Tip: To customize the themes, change the last line of the prompt. For example, you can modify the focus to other themes like "Friendship, Growth, Resilience." Replace
"Love, Heartbreak, Romance"with your chosen themes.
This setup will allow you to use the model efficiently and generate lyrics in the style of Taylor Swift.
Uploaded model
- Developed by: Covvenheimer
- License: apache-2.0
- Finetuned from model : unsloth/llama-3.2-1b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
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