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/src/llama-impl.h from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.87 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/src/llama-impl.h
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/src/llama-impl.h
-
curl -L -o llama-impl.h https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/src/llama-impl.h
1.87 kB
| // | |
| // logging | |
| // | |
| LLAMA_ATTRIBUTE_FORMAT(2, 3) | |
| void llama_log_internal (ggml_log_level level, const char * format, ...); | |
| void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data); | |
| // | |
| // helpers | |
| // | |
| template <typename T> | |
| struct no_init { | |
| T value; | |
| no_init() { /* do nothing */ } | |
| }; | |
| struct time_meas { | |
| time_meas(int64_t & t_acc, bool disable = false); | |
| ~time_meas(); | |
| const int64_t t_start_us; | |
| int64_t & t_acc; | |
| }; | |
| void replace_all(std::string & s, const std::string & search, const std::string & replace); | |
| // TODO: rename to llama_format ? | |
| LLAMA_ATTRIBUTE_FORMAT(1, 2) | |
| std::string format(const char * fmt, ...); | |
| std::string llama_format_tensor_shape(const std::vector<int64_t> & ne); | |
| std::string llama_format_tensor_shape(const struct ggml_tensor * t); | |
| std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i); | |