Instructions to use Fantominsight/YAPPERTAR-code-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Fantominsight/YAPPERTAR-code-v1 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Fantominsight/YAPPERTAR-code-v1", filename="YAPPERTAR-coder.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Fantominsight/YAPPERTAR-code-v1 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Fantominsight/YAPPERTAR-code-v1 # Run inference directly in the terminal: llama-cli -hf Fantominsight/YAPPERTAR-code-v1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Fantominsight/YAPPERTAR-code-v1 # Run inference directly in the terminal: llama-cli -hf Fantominsight/YAPPERTAR-code-v1
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 Fantominsight/YAPPERTAR-code-v1 # Run inference directly in the terminal: ./llama-cli -hf Fantominsight/YAPPERTAR-code-v1
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 Fantominsight/YAPPERTAR-code-v1 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Fantominsight/YAPPERTAR-code-v1
Use Docker
docker model run hf.co/Fantominsight/YAPPERTAR-code-v1
- LM Studio
- Jan
- Ollama
How to use Fantominsight/YAPPERTAR-code-v1 with Ollama:
ollama run hf.co/Fantominsight/YAPPERTAR-code-v1
- Unsloth Studio new
How to use Fantominsight/YAPPERTAR-code-v1 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 Fantominsight/YAPPERTAR-code-v1 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 Fantominsight/YAPPERTAR-code-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Fantominsight/YAPPERTAR-code-v1 to start chatting
- Docker Model Runner
How to use Fantominsight/YAPPERTAR-code-v1 with Docker Model Runner:
docker model run hf.co/Fantominsight/YAPPERTAR-code-v1
- Lemonade
How to use Fantominsight/YAPPERTAR-code-v1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Fantominsight/YAPPERTAR-code-v1
Run and chat with the model
lemonade run user.YAPPERTAR-code-v1-{{QUANT_TAG}}List all available models
lemonade list
- Xet hash:
- 51888f70f5f25c04f42844093e39053fc98c4df7a42ccda2679c9b755039228d
- Size of remote file:
- 4.7 GB
- SHA256:
- 8b10be8ee7da0df43fb8615f0aaf80cb2fa44d17517c7d2f28ddfa78f4d78b11
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