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-cparams.h from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/src/llama-cparams.h
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/src/llama-cparams.h
-
curl -L -o llama-cparams.h https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/src/llama-cparams.h
1.06 kB
| struct llama_cparams { | |
| uint32_t n_ctx; // context size used during inference | |
| uint32_t n_batch; | |
| uint32_t n_ubatch; | |
| uint32_t n_seq_max; | |
| int32_t n_threads; // number of threads to use for generation | |
| int32_t n_threads_batch; // number of threads to use for batch processing | |
| float rope_freq_base; | |
| float rope_freq_scale; | |
| uint32_t n_ctx_orig_yarn; | |
| // These hyperparameters are not exposed in GGUF, because all | |
| // existing YaRN models use the same values for them. | |
| float yarn_ext_factor; | |
| float yarn_attn_factor; | |
| float yarn_beta_fast; | |
| float yarn_beta_slow; | |
| float defrag_thold; | |
| bool embeddings; | |
| bool causal_attn; | |
| bool offload_kqv; | |
| bool flash_attn; | |
| bool no_perf; | |
| bool warmup; | |
| bool op_offload; | |
| bool kv_unified; | |
| enum llama_pooling_type pooling_type; | |
| ggml_backend_sched_eval_callback cb_eval; | |
| void * cb_eval_user_data; | |
| }; | |