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
|
Download phi-coder-hf/llama.cpp/tools/server/tests/README.md from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 2.45 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/server/tests/README.md
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/tools/server/tests/README.md
-
curl -L -o README.md https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/server/tests/README.md
2.45 kB
| # Server tests | |
| Python based server tests scenario using [pytest](https://docs.pytest.org/en/stable/). | |
| Tests target GitHub workflows job runners with 4 vCPU. | |
| Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. | |
| To mitigate it, you can increase values in `n_predict`, `kv_size`. | |
| ### Install dependencies | |
| `pip install -r requirements.txt` | |
| ### Run tests | |
| 1. Build the server | |
| ```shell | |
| cd ../../.. | |
| cmake -B build | |
| cmake --build build --target llama-server | |
| ``` | |
| 2. Start the test: `./tests.sh` | |
| It's possible to override some scenario steps values with environment variables: | |
| | variable | description | | |
| |--------------------------|------------------------------------------------------------------------------------------------| | |
| | `PORT` | `context.server_port` to set the listening port of the server during scenario, default: `8080` | | |
| | `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/llama-server` | | |
| | `DEBUG` | to enable steps and server verbose mode `--verbose` | | |
| | `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` | | |
| | `LLAMA_CACHE` | by default server tests re-download models to the `tmp` subfolder. Set this to your cache (e.g. `$HOME/Library/Caches/llama.cpp` on Mac or `$HOME/.cache/llama.cpp` on Unix) to avoid this | | |
| To run slow tests (will download many models, make sure to set `LLAMA_CACHE` if needed): | |
| ```shell | |
| SLOW_TESTS=1 ./tests.sh | |
| ``` | |
| To run with stdout/stderr display in real time (verbose output, but useful for debugging): | |
| ```shell | |
| DEBUG=1 ./tests.sh -s -v -x | |
| ``` | |
| To run all the tests in a file: | |
| ```shell | |
| ./tests.sh unit/test_chat_completion.py -v -x | |
| ``` | |
| To run a single test: | |
| ```shell | |
| ./tests.sh unit/test_chat_completion.py::test_invalid_chat_completion_req | |
| ``` | |
| Hint: You can compile and run test in single command, useful for local developement: | |
| ```shell | |
| cmake --build build -j --target llama-server && ./tools/server/tests/tests.sh | |
| ``` | |
| To see all available arguments, please refer to [pytest documentation](https://docs.pytest.org/en/stable/how-to/usage.html) | |