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/tests/test-barrier.cpp from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 2.79 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tests/test-barrier.cpp
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/tests/test-barrier.cpp
-
curl -L -o test-barrier.cpp https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tests/test-barrier.cpp
2.79 kB
| int main(int argc, char *argv[]) { | |
| int n_threads = 4; | |
| int n_rounds = 100; | |
| if (argc > 1) { | |
| n_threads = std::atoi(argv[1]); | |
| } | |
| if (argc > 2) { | |
| n_rounds = std::atoi(argv[2]); | |
| } | |
| struct ggml_init_params params = { | |
| /* .mem_size = */ 1024*1024*1024, | |
| /* .mem_buffer = */ NULL, | |
| /* .no_alloc = */ false, | |
| }; | |
| struct ggml_context * ctx = ggml_init(params); | |
| // Create graph | |
| struct ggml_cgraph * gf = ggml_new_graph(ctx); | |
| // Lots of small, parallel ops where barriers in between will dominate | |
| struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 64); | |
| for (int i = 0; i < 1000; i++) { | |
| struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128); | |
| out = ggml_mul_mat(ctx, a, out); | |
| struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64); | |
| out = ggml_mul_mat(ctx, d, out); | |
| } | |
| ggml_build_forward_expand(gf, out); | |
| int n_nodes = ggml_graph_n_nodes(gf); | |
| // Create threadpool | |
| struct ggml_threadpool_params tpp = ggml_threadpool_params_default(n_threads); | |
| struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp); | |
| if (!threadpool) { | |
| fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads); | |
| exit(1); | |
| } | |
| // Create compute plan | |
| struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads, threadpool); | |
| std::vector<uint8_t> work_data(cplan.work_size); | |
| cplan.work_data = work_data.data(); | |
| std::cerr << "graph-compute with" | |
| << "\n n_threads: " << n_threads | |
| << "\n n_nodes: " << n_nodes | |
| << "\n n_rounds: " << n_rounds | |
| << "\n"; | |
| // ggml_graph_print(gf); | |
| // Warmup | |
| ggml_graph_compute(gf, &cplan); | |
| auto t0 = std::chrono::high_resolution_clock::now(); | |
| for (int i=0; i < n_rounds; i++) { | |
| ggml_graph_compute(gf, &cplan); | |
| } | |
| auto t1 = std::chrono::high_resolution_clock::now(); | |
| auto usec = std::chrono::duration_cast<std::chrono::microseconds>(t1-t0).count(); | |
| auto nsec = std::chrono::duration_cast<std::chrono::nanoseconds>(t1-t0).count(); | |
| std::cerr << "graph-compute took " << usec << " usec " | |
| << "\n " << (float) usec / n_rounds << " usec per-iter" | |
| << "\n " << (float) nsec / (n_rounds * n_nodes) << " nsec per-node" | |
| << "\n"; | |
| ggml_threadpool_free(threadpool); | |
| ggml_free(ctx); | |
| return 0; | |
| } | |