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/tools/export-lora/README.md from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/export-lora/README.md
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/tools/export-lora/README.md
-
curl -L -o README.md https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/export-lora/README.md
1.17 kB
| # export-lora | |
| Apply LORA adapters to base model and export the resulting model. | |
| ``` | |
| usage: llama-export-lora [options] | |
| options: | |
| -m, --model model path from which to load base model (default '') | |
| --lora FNAME path to LoRA adapter (can be repeated to use multiple adapters) | |
| --lora-scaled FNAME S path to LoRA adapter with user defined scaling S (can be repeated to use multiple adapters) | |
| -t, --threads N number of threads to use during computation (default: 4) | |
| -o, --output FNAME output file (default: 'ggml-lora-merged-f16.gguf') | |
| ``` | |
| For example: | |
| ```bash | |
| ./bin/llama-export-lora \ | |
| -m open-llama-3b-v2.gguf \ | |
| -o open-llama-3b-v2-english2tokipona-chat.gguf \ | |
| --lora lora-open-llama-3b-v2-english2tokipona-chat-LATEST.gguf | |
| ``` | |
| Multiple LORA adapters can be applied by passing multiple `--lora FNAME` or `--lora-scaled FNAME S` command line parameters: | |
| ```bash | |
| ./bin/llama-export-lora \ | |
| -m your_base_model.gguf \ | |
| -o your_merged_model.gguf \ | |
| --lora-scaled lora_task_A.gguf 0.5 \ | |
| --lora-scaled lora_task_B.gguf 0.5 | |
| ``` | |