- Libraries
- Transformers
How to use second-state/SmolVLM2-256M-Video-Instruct-GGUF with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("image-text-to-text", model="second-state/SmolVLM2-256M-Video-Instruct-GGUF")
messages = [
{
"role": "user",
"content": [
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
{"type": "text", "text": "What animal is on the candy?"}
]
},
]
pipe(text=messages) # Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM
processor = AutoProcessor.from_pretrained("second-state/SmolVLM2-256M-Video-Instruct-GGUF")
model = AutoModelForMultimodalLM.from_pretrained("second-state/SmolVLM2-256M-Video-Instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use second-state/SmolVLM2-256M-Video-Instruct-GGUF 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 second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/SmolVLM2-256M-Video-Instruct-GGUF: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 second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/SmolVLM2-256M-Video-Instruct-GGUF: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 second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use second-state/SmolVLM2-256M-Video-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/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": "second-state/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"
}
}
]
}
]
}'Use Docker
docker model run hf.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
- SGLang
How to use second-state/SmolVLM2-256M-Video-Instruct-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "second-state/SmolVLM2-256M-Video-Instruct-GGUF" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "second-state/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"
}
}
]
}
]
}'Use Docker images
docker run --gpus all \
--shm-size 32g \
-p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
--ipc=host \
lmsysorg/sglang:latest \
python3 -m sglang.launch_server \
--model-path "second-state/SmolVLM2-256M-Video-Instruct-GGUF" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "second-state/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"
}
}
]
}
]
}' - Ollama
How to use second-state/SmolVLM2-256M-Video-Instruct-GGUF with Ollama:
ollama run hf.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use second-state/SmolVLM2-256M-Video-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
- Lemonade
How to use second-state/SmolVLM2-256M-Video-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/SmolVLM2-256M-Video-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.SmolVLM2-256M-Video-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
