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
File size: 5,336 Bytes
9b19fd8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | #!/usr/bin/env bash
# make sure we are in the right directory
SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
cd $SCRIPT_DIR
#export LLAMA_CACHE="$SCRIPT_DIR/tmp"
set -eux
mkdir -p $SCRIPT_DIR/output
PROJ_ROOT="$SCRIPT_DIR/../.."
cd $PROJ_ROOT
# Check if the first argument is "big", then run test with big models
# This is useful if we're running the script on a larger machine, so we can test the big models
RUN_BIG_TESTS=false
if [ "${1:-}" = "big" ]; then
RUN_BIG_TESTS=true
echo "Include BIG models..."
fi
RUN_HUGE_TESTS=false
if [ "${1:-}" = "huge" ]; then
RUN_HUGE_TESTS=true
RUN_BIG_TESTS=true
echo "Include BIG and HUGE models..."
fi
###############
arr_prefix=()
arr_hf=()
arr_tmpl=() # chat template
arr_file=()
add_test_vision() {
local hf=$1
local tmpl=${2:-""} # default to empty string if not provided
arr_prefix+=("[vision]")
arr_hf+=("$hf")
arr_tmpl+=("$tmpl")
arr_file+=("test-1.jpeg")
}
add_test_audio() {
local hf=$1
arr_prefix+=("[audio] ")
arr_hf+=("$hf")
arr_tmpl+=("") # no need for chat tmpl
arr_file+=("test-2.mp3")
}
add_test_vision "ggml-org/SmolVLM-500M-Instruct-GGUF:Q8_0"
add_test_vision "ggml-org/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/SmolVLM2-500M-Video-Instruct-GGUF:Q8_0"
add_test_vision "ggml-org/gemma-3-4b-it-GGUF:Q4_K_M"
add_test_vision "THUDM/glm-edge-v-5b-gguf:Q4_K_M"
add_test_vision "second-state/Llava-v1.5-7B-GGUF:Q2_K" "vicuna"
add_test_vision "cjpais/llava-1.6-mistral-7b-gguf:Q3_K_M" "vicuna"
add_test_vision "ibm-research/granite-vision-3.2-2b-GGUF:Q4_K_M"
add_test_vision "second-state/MiniCPM-Llama3-V-2_5-GGUF:Q2_K" # model from openbmb is corrupted
add_test_vision "openbmb/MiniCPM-V-2_6-gguf:Q2_K"
add_test_vision "openbmb/MiniCPM-o-2_6-gguf:Q4_0"
add_test_vision "bartowski/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/InternVL2_5-1B-GGUF:Q8_0"
add_test_vision "ggml-org/InternVL3-1B-Instruct-GGUF:Q8_0"
add_test_vision "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"
add_test_audio "ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF:Q8_0"
add_test_audio "ggml-org/Qwen2.5-Omni-3B-GGUF:Q4_K_M"
add_test_audio "ggml-org/Voxtral-Mini-3B-2507-GGUF:Q4_K_M"
# to test the big models, run: ./tests.sh big
if [ "$RUN_BIG_TESTS" = true ]; then
add_test_vision "ggml-org/pixtral-12b-GGUF:Q4_K_M"
add_test_vision "ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF" "mistral-v7"
add_test_vision "ggml-org/Qwen2-VL-2B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2-VL-7B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2.5-VL-3B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2.5-VL-7B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/InternVL3-8B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/InternVL3-14B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
# add_test_vision "ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M" # does not work on my mac M3 Ultra
add_test_audio "ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF:Q4_K_M"
add_test_audio "ggml-org/Qwen2.5-Omni-7B-GGUF:Q4_K_M"
fi
# to test the huge models, run: ./tests.sh huge
# this will run both the big and huge models
# huge models are > 32B parameters
if [ "$RUN_HUGE_TESTS" = true ]; then
add_test_vision "ggml-org/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M"
add_test_vision "ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF:IQ1_S"
fi
# these models always give the wrong answer, not sure why
# add_test_vision "ggml-org/SmolVLM-Instruct-GGUF:Q4_K_M"
# add_test_vision "ggml-org/SmolVLM-256M-Instruct-GGUF:Q8_0"
# add_test_vision "ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q8_0"
# this model has broken chat template, not usable
# add_test_vision "cmp-nct/Yi-VL-6B-GGUF:Q5_K"
# add_test_vision "guinmoon/MobileVLM-3B-GGUF:Q4_K_M" "deepseek"
###############
cmake --build build -j --target llama-mtmd-cli
arr_res=()
for i in "${!arr_hf[@]}"; do
bin="llama-mtmd-cli"
prefix="${arr_prefix[$i]}"
hf="${arr_hf[$i]}"
tmpl="${arr_tmpl[$i]}"
inp_file="${arr_file[$i]}"
echo "Running test with binary: $bin and HF model: $hf"
echo ""
echo ""
output=$(\
"$PROJ_ROOT/build/bin/$bin" \
-hf "$hf" \
--image $SCRIPT_DIR/$inp_file \
-p "what is the publisher name of the newspaper?" \
--temp 0 -n 128 \
${tmpl:+--chat-template "$tmpl"} \
2>&1 | tee /dev/tty)
echo "$output" > $SCRIPT_DIR/output/$bin-$(echo "$hf" | tr '/' '-').log
if echo "$output" | grep -iq "new york"; then
result="$prefix \033[32mOK\033[0m: $bin $hf"
else
result="$prefix \033[31mFAIL\033[0m: $bin $hf"
fi
echo -e "$result"
arr_res+=("$result")
echo ""
echo ""
echo ""
echo "#################################################"
echo "#################################################"
echo ""
echo ""
done
set +x
for i in "${!arr_res[@]}"; do
echo -e "${arr_res[$i]}"
done
echo ""
echo "Output logs are saved in $SCRIPT_DIR/output"
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