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
- Llama new
- 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/examples/sycl/README.md from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.5 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/sycl/README.md
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/examples/sycl/README.md
-
curl -L -o README.md https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/sycl/README.md
1.5 kB
| # llama.cpp/example/sycl | |
| This example program provides the tools for llama.cpp for SYCL on Intel GPU. | |
| ## Tool | |
| |Tool Name| Function|Status| | |
| |-|-|-| | |
| |llama-ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support| | |
| ### llama-ls-sycl-device | |
| List all SYCL devices with ID, compute capability, max work group size, ect. | |
| 1. Build the llama.cpp for SYCL for the specified target *(using GGML_SYCL_TARGET)*. | |
| 2. Enable oneAPI running environment *(if GGML_SYCL_TARGET is set to INTEL -default-)* | |
| ``` | |
| source /opt/intel/oneapi/setvars.sh | |
| ``` | |
| 3. Execute | |
| ``` | |
| ./build/bin/llama-ls-sycl-device | |
| ``` | |
| Check the ID in startup log, like: | |
| ``` | |
| found 2 SYCL devices: | |
| | | | | |Max | |Max |Global | | | |
| | | | | |compute|Max work|sub |mem | | | |
| |ID| Device Type| Name|Version|units |group |group|size | Driver version| | |
| |--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------| | |
| | 0| [level_zero:gpu:0]| Intel Arc A770 Graphics| 1.3| 512| 1024| 32| 16225M| 1.3.29138| | |
| | 1| [level_zero:gpu:1]| Intel UHD Graphics 750| 1.3| 32| 512| 32| 62631M| 1.3.29138| | |
| ``` | |