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/quantize/tests.sh from AJKADZ/PHI_CODER: direct link, hf CLI and curl.
- Browser
- Download file 1.63 kB
-
https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/quantize/tests.sh
- Command line
-
hf download hf://AJKADZ/PHI_CODER/phi-coder-hf/llama.cpp/tools/quantize/tests.sh
-
curl -L -o tests.sh https://huggingface.co/AJKADZ/PHI_CODER/resolve/main/phi-coder-hf/llama.cpp/tools/quantize/tests.sh
1.63 kB
| set -eu | |
| if [ $# -lt 1 ] | |
| then | |
| echo "usage: $0 path_to_build_binary [path_to_temp_folder]" | |
| echo "example: $0 ../../build/bin ../../tmp" | |
| exit 1 | |
| fi | |
| if [ $# -gt 1 ] | |
| then | |
| TMP_DIR=$2 | |
| else | |
| TMP_DIR=/tmp | |
| fi | |
| set -x | |
| SPLIT=$1/llama-gguf-split | |
| QUANTIZE=$1/llama-quantize | |
| MAIN=$1/llama-cli | |
| WORK_PATH=$TMP_DIR/quantize | |
| ROOT_DIR=$(realpath $(dirname $0)/../../) | |
| mkdir -p "$WORK_PATH" | |
| # Clean up in case of previously failed test | |
| rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf | |
| # 1. Get a model | |
| ( | |
| cd $WORK_PATH | |
| "$ROOT_DIR"/scripts/hf.sh --repo ggml-org/gemma-1.1-2b-it-Q8_0-GGUF --file gemma-1.1-2b-it.Q8_0.gguf | |
| ) | |
| echo PASS | |
| # 2. Split model | |
| $SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/ggml-model-split | |
| echo PASS | |
| echo | |
| # 3. Requant model with '--keep-split' | |
| $QUANTIZE --allow-requantize --keep-split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K | |
| echo PASS | |
| echo | |
| # 3a. Test the requanted model is loading properly | |
| $MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32 | |
| echo PASS | |
| echo | |
| # 4. Requant mode without '--keep-split' | |
| $QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K | |
| echo PASS | |
| echo | |
| # 4b. Test the requanted model is loading properly | |
| $MAIN -no-cnv --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32 | |
| echo PASS | |
| echo | |
| # Clean up | |
| rm -f $WORK_PATH/ggml-model-split*.gguf $WORK_PATH/ggml-model-requant*.gguf | |