Instructions to use TheBloke/Python-Code-13B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/Python-Code-13B-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("TheBloke/Python-Code-13B-GGUF", dtype="auto") - llama-cpp-python
How to use TheBloke/Python-Code-13B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TheBloke/Python-Code-13B-GGUF", filename="python-code-13b.Q2_K.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 TheBloke/Python-Code-13B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TheBloke/Python-Code-13B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf TheBloke/Python-Code-13B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf TheBloke/Python-Code-13B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf TheBloke/Python-Code-13B-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 TheBloke/Python-Code-13B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf TheBloke/Python-Code-13B-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 TheBloke/Python-Code-13B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf TheBloke/Python-Code-13B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/TheBloke/Python-Code-13B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use TheBloke/Python-Code-13B-GGUF with Ollama:
ollama run hf.co/TheBloke/Python-Code-13B-GGUF:Q4_K_M
- Unsloth Studio new
How to use TheBloke/Python-Code-13B-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 TheBloke/Python-Code-13B-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 TheBloke/Python-Code-13B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TheBloke/Python-Code-13B-GGUF to start chatting
- Docker Model Runner
How to use TheBloke/Python-Code-13B-GGUF with Docker Model Runner:
docker model run hf.co/TheBloke/Python-Code-13B-GGUF:Q4_K_M
- Lemonade
How to use TheBloke/Python-Code-13B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TheBloke/Python-Code-13B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Python-Code-13B-GGUF-Q4_K_M
List all available models
lemonade list
Blank first response - just an FYI of what works for me
Not a complaint at all, we all appreciate what is being done here and most of us do understand you do not create the original model, and this is just an observation. Recently I noticed that sometimes i first get a blank response from several "python code gen" targeted models, including this one. I saw this even before GGUF format became a thing. But, if i hit continue then it starts responding like it should. ( ooba has this feature, not all web UIs do, unfortunately )
Also, i had to switch to using vicuna1.1 prompt format for many models, even if the source has other ones listed ( especially when it lists alpaca, that never seems to work right for me ). Else i get garbage responses, either not answering the question or just true random nonsense. I also end up using the stock llama-precise parameters. Again, or i get garbage. So not a big deal, just an observation of what works for me in many cases if others see this too.
And again, @TheBloke thanks for all you do for the community.