How to use from
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 ankurkaul17/minicpm-v-4.6-pid-gguf:F16
# Run inference directly in the terminal:
llama cli -hf ankurkaul17/minicpm-v-4.6-pid-gguf:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ankurkaul17/minicpm-v-4.6-pid-gguf:F16
# Run inference directly in the terminal:
llama cli -hf ankurkaul17/minicpm-v-4.6-pid-gguf:F16
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 ankurkaul17/minicpm-v-4.6-pid-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf ankurkaul17/minicpm-v-4.6-pid-gguf:F16
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 ankurkaul17/minicpm-v-4.6-pid-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ankurkaul17/minicpm-v-4.6-pid-gguf:F16
Use Docker
docker model run hf.co/ankurkaul17/minicpm-v-4.6-pid-gguf:F16
Quick Links

Finetuned MiniCPM-V-4.6 (Plant Identification - GGUF)

GGUF quantized versions of the fine-tuned MiniCPM-V-4.6 model for plant identification.

Files

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plant-id-v46-4x-lora-F16.gguf FP16 ~1.5 GB
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mmproj-plant-id-v46-4x-lora-F16.gguf Vision Projector (FP16) ~1.1 GB
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