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/examples/lookup/lookup-create.cpp from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.15 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/lookup/lookup-create.cpp
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/examples/lookup/lookup-create.cpp
-
curl -L -o lookup-create.cpp https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/lookup/lookup-create.cpp
1.15 kB
| int main(int argc, char ** argv){ | |
| common_params params; | |
| if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_LOOKUP)) { | |
| return 1; | |
| } | |
| // init llama.cpp | |
| llama_backend_init(); | |
| llama_numa_init(params.numa); | |
| // load the model | |
| common_init_result llama_init = common_init_from_params(params); | |
| llama_model_ptr & model = llama_init.model; | |
| llama_context_ptr & ctx = llama_init.context; | |
| GGML_ASSERT(model != nullptr); | |
| // tokenize the prompt | |
| std::vector<llama_token> inp; | |
| inp = common_tokenize(ctx.get(), params.prompt, true, true); | |
| fprintf(stderr, "%s: tokenization done\n", __func__); | |
| common_ngram_cache ngram_cache; | |
| common_ngram_cache_update(ngram_cache, LLAMA_NGRAM_STATIC, LLAMA_NGRAM_STATIC, inp, inp.size(), true); | |
| fprintf(stderr, "%s: hashing done, writing file to %s\n", __func__, params.lookup_cache_static.c_str()); | |
| common_ngram_cache_save(ngram_cache, params.lookup_cache_static); | |
| return 0; | |
| } | |