SmolVLM GGUF
Collection
6 items โข Updated โข 7
How to use ggml-org/SmolVLM2-256M-Video-Instruct-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
docker model run hf.co/ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
How to use ggml-org/SmolVLM2-256M-Video-Instruct-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ggml-org/SmolVLM2-256M-Video-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "ggml-org/SmolVLM2-256M-Video-Instruct-GGUF",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "Describe this image in one sentence."
},
{
"type": "image_url",
"image_url": {
"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
}
}
]
}
]
}'docker model run hf.co/ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
How to use ggml-org/SmolVLM2-256M-Video-Instruct-GGUF with Ollama:
ollama run hf.co/ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
How to use ggml-org/SmolVLM2-256M-Video-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
How to use ggml-org/SmolVLM2-256M-Video-Instruct-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ggml-org/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
lemonade run user.SmolVLM2-256M-Video-Instruct-GGUF-Q4_K_M
lemonade list
Run it in the Llama app: https://llama.app
Or from the terminal:
llama serve -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF
This model is automatically converted using https://github.com/ggml-org/convert
4-bit
8-bit
16-bit
Base model
HuggingFaceTB/SmolLM2-135M