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/ggml/src/ggml-cpu/traits.cpp from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.27 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/ggml/src/ggml-cpu/traits.cpp
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/ggml/src/ggml-cpu/traits.cpp
-
curl -L -o traits.cpp https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/ggml/src/ggml-cpu/traits.cpp
1.27 kB
| namespace ggml::cpu { | |
| tensor_traits::~tensor_traits() {} | |
| extra_buffer_type::~extra_buffer_type() {} | |
| } // namespace ggml::cpu | |
| bool ggml_cpu_extra_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * op) { | |
| for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) { | |
| if (extra && extra->context) { | |
| auto buf_extra = (ggml::cpu::extra_buffer_type *) extra->context; | |
| auto tensor_traits = buf_extra->get_tensor_traits(op); | |
| if (tensor_traits && tensor_traits->compute_forward(params, op)) { | |
| return true; | |
| } | |
| } | |
| } | |
| return false; | |
| } | |
| bool ggml_cpu_extra_work_size(int n_threads, const struct ggml_tensor * op, size_t * size) { | |
| for (auto extra : ggml_backend_cpu_get_extra_buffer_types()) { | |
| if (extra && extra->context) { | |
| auto buf_extra = (ggml::cpu::extra_buffer_type *) extra->context; | |
| auto tensor_traits = buf_extra->get_tensor_traits(op); | |
| if (tensor_traits && tensor_traits->work_size(n_threads, op, *size)) { | |
| return true; | |
| } | |
| } | |
| } | |
| return false; | |
| } | |