Instructions to use AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER: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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AJKADZ/PHI_CODER: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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AJKADZ/PHI_CODER:Q4_K_M
Use Docker
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AJKADZ/PHI_CODER with Ollama:
ollama run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use AJKADZ/PHI_CODER with Docker Model Runner:
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Lemonade
How to use AJKADZ/PHI_CODER with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AJKADZ/PHI_CODER:Q4_K_M
Run and chat with the model
lemonade run user.PHI_CODER-Q4_K_M
List all available models
lemonade list
- Atomic Chat
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Download phi-coder-hf/llama.cpp/docs/multimodal/gemma3.md from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.2 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/docs/multimodal/gemma3.md
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/docs/multimodal/gemma3.md
-
curl -L -o gemma3.md https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/docs/multimodal/gemma3.md
1.2 kB
Gemma 3 vision
This is very experimental, only used for demo purpose.
Quick started
You can use pre-quantized model from ggml-org's Hugging Face account
# build
cmake -B build
cmake --build build --target llama-mtmd-cli
# alternatively, install from brew (MacOS)
brew install llama.cpp
# run it
llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF
llama-mtmd-cli -hf ggml-org/gemma-3-12b-it-GGUF
llama-mtmd-cli -hf ggml-org/gemma-3-27b-it-GGUF
# note: 1B model does not support vision
How to get mmproj.gguf?
Simply to add --mmproj in when converting model via convert_hf_to_gguf.py:
cd gemma-3-4b-it
python ../llama.cpp/convert_hf_to_gguf.py --outfile model.gguf --outtype f16 --mmproj .
# output file: mmproj-model.gguf
How to run it?
What you need:
- The text model GGUF, can be converted using
convert_hf_to_gguf.py - The mmproj file from step above
- An image file
# build
cmake -B build
cmake --build build --target llama-mtmd-cli
# run it
./build/bin/llama-mtmd-cli -m {text_model}.gguf --mmproj mmproj.gguf --image your_image.jpg