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/scripts/ci-run.sh from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.37 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/scripts/ci-run.sh
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/scripts/ci-run.sh
-
curl -L -o ci-run.sh https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/scripts/ci-run.sh
1.37 kB
| set -euo pipefail | |
| this=$(realpath "$0"); readonly this | |
| cd "$(dirname "$this")" | |
| shellcheck "$this" | |
| if (( $# != 1 && $# != 2 )); then | |
| cat >&2 <<'EOF' | |
| usage: | |
| ci-run.sh <tmp_dir> [<cache_dir>] | |
| This script wraps ci/run.sh: | |
| * If <tmp_dir> is a ramdisk, you can reduce writes to your SSD. If <tmp_dir> is not a ramdisk, keep in mind that total writes will increase by the size of <cache_dir>. | |
| (openllama_3b_v2: quantized models are about 30GB) | |
| * Persistent model and data files are synced to and from <cache_dir>, | |
| excluding generated .gguf files. | |
| (openllama_3b_v2: persistent files are about 6.6GB) | |
| * <cache_dir> defaults to ~/.cache/llama.cpp | |
| EOF | |
| exit 1 | |
| fi | |
| cd .. # => llama.cpp repo root | |
| tmp="$1" | |
| mkdir -p "$tmp" | |
| tmp=$(realpath "$tmp") | |
| echo >&2 "Using tmp=$tmp" | |
| cache="${2-$HOME/.cache/llama.cpp}" | |
| mkdir -p "$cache" | |
| cache=$(realpath "$cache") | |
| echo >&2 "Using cache=$cache" | |
| _sync() { | |
| local from="$1"; shift | |
| local to="$1"; shift | |
| echo >&2 "Syncing from $from to $to" | |
| mkdir -p "$from" "$to" | |
| rsync -a "$from" "$to" --delete-during "$@" | |
| } | |
| _sync "$(realpath .)/" "$tmp/llama.cpp" | |
| _sync "$cache/ci-mnt/models/" "$tmp/llama.cpp/ci-mnt/models/" | |
| cd "$tmp/llama.cpp" | |
| bash ci/run.sh ci-out ci-mnt | |
| _sync 'ci-mnt/models/' "$cache/ci-mnt/models/" --exclude='*.gguf' -P | |