Instructions to use mjschock/SmolVLM2-500M-Video-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use mjschock/SmolVLM2-500M-Video-Instruct with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mjschock/SmolVLM2-500M-Video-Instruct", filename="SmolVLM2-500M-Video-Instruct-Q8_0.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use mjschock/SmolVLM2-500M-Video-Instruct with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mjschock/SmolVLM2-500M-Video-Instruct:Q8_0 # Run inference directly in the terminal: llama-cli -hf mjschock/SmolVLM2-500M-Video-Instruct:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mjschock/SmolVLM2-500M-Video-Instruct:Q8_0 # Run inference directly in the terminal: llama-cli -hf mjschock/SmolVLM2-500M-Video-Instruct:Q8_0
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 mjschock/SmolVLM2-500M-Video-Instruct:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf mjschock/SmolVLM2-500M-Video-Instruct:Q8_0
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 mjschock/SmolVLM2-500M-Video-Instruct:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf mjschock/SmolVLM2-500M-Video-Instruct:Q8_0
Use Docker
docker model run hf.co/mjschock/SmolVLM2-500M-Video-Instruct:Q8_0
- LM Studio
- Jan
- Ollama
How to use mjschock/SmolVLM2-500M-Video-Instruct with Ollama:
ollama run hf.co/mjschock/SmolVLM2-500M-Video-Instruct:Q8_0
- Unsloth Studio new
How to use mjschock/SmolVLM2-500M-Video-Instruct with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mjschock/SmolVLM2-500M-Video-Instruct to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mjschock/SmolVLM2-500M-Video-Instruct to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mjschock/SmolVLM2-500M-Video-Instruct to start chatting
- Docker Model Runner
How to use mjschock/SmolVLM2-500M-Video-Instruct with Docker Model Runner:
docker model run hf.co/mjschock/SmolVLM2-500M-Video-Instruct:Q8_0
- Lemonade
How to use mjschock/SmolVLM2-500M-Video-Instruct with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mjschock/SmolVLM2-500M-Video-Instruct:Q8_0
Run and chat with the model
lemonade run user.SmolVLM2-500M-Video-Instruct-Q8_0
List all available models
lemonade list
File size: 599 Bytes
9e6d11f | 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 | {
"do_convert_rgb": true,
"do_image_splitting": true,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "SmolVLMImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_image_size": {
"longest_edge": 512
},
"processor_class": "SmolVLMProcessor",
"resample": 1,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 2048
},
"video_sampling": {
"fps": 1,
"max_frames": 64,
"video_size": {
"longest_edge": 512
}
}
}
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