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/chat-13B.sh from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/chat-13B.sh
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/examples/chat-13B.sh
-
curl -L -o chat-13B.sh https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/examples/chat-13B.sh
1.39 kB
| set -e | |
| cd "$(dirname "$0")/.." || exit | |
| MODEL="${MODEL:-./models/13B/ggml-model-q4_0.bin}" | |
| PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt} | |
| USER_NAME="${USER_NAME:-USER}" | |
| AI_NAME="${AI_NAME:-ChatLLaMa}" | |
| # Adjust to the number of CPU cores you want to use. | |
| N_THREAD="${N_THREAD:-8}" | |
| # Number of tokens to predict (made it larger than default because we want a long interaction) | |
| N_PREDICTS="${N_PREDICTS:-2048}" | |
| # Note: you can also override the generation options by specifying them on the command line: | |
| # For example, override the context size by doing: ./chatLLaMa --ctx_size 1024 | |
| GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}" | |
| DATE_TIME=$(date +%H:%M) | |
| DATE_YEAR=$(date +%Y) | |
| PROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt) | |
| sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \ | |
| -e "s/\[\[AI_NAME\]\]/$AI_NAME/g" \ | |
| -e "s/\[\[DATE_TIME\]\]/$DATE_TIME/g" \ | |
| -e "s/\[\[DATE_YEAR\]\]/$DATE_YEAR/g" \ | |
| $PROMPT_TEMPLATE > $PROMPT_FILE | |
| # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS | |
| ./llama-cli $GEN_OPTIONS \ | |
| --model "$MODEL" \ | |
| --threads "$N_THREAD" \ | |
| --n_predict "$N_PREDICTS" \ | |
| --color --interactive \ | |
| --file ${PROMPT_FILE} \ | |
| --reverse-prompt "${USER_NAME}:" \ | |
| --in-prefix ' ' \ | |
| "$@" | |