Instructions to use anquachdev/SmolVLM-500M-Instruct-GGUF 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 anquachdev/SmolVLM-500M-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 anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf anquachdev/SmolVLM-500M-Instruct-GGUF: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 anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf anquachdev/SmolVLM-500M-Instruct-GGUF: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 anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0
Use Docker
docker model run hf.co/anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use anquachdev/SmolVLM-500M-Instruct-GGUF with Ollama:
ollama run hf.co/anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0
- Unsloth Studio
How to use anquachdev/SmolVLM-500M-Instruct-GGUF 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 anquachdev/SmolVLM-500M-Instruct-GGUF 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 anquachdev/SmolVLM-500M-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for anquachdev/SmolVLM-500M-Instruct-GGUF to start chatting
- Docker Model Runner
How to use anquachdev/SmolVLM-500M-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0
- Lemonade
How to use anquachdev/SmolVLM-500M-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull anquachdev/SmolVLM-500M-Instruct-GGUF:Q8_0
Run and chat with the model
lemonade run user.SmolVLM-500M-Instruct-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
Commit ·
d669be7
0
Parent(s):
Duplicate from ggml-org/SmolVLM-500M-Instruct-GGUF
Browse filesCo-authored-by: Xuan-Son Nguyen <ngxson@users.noreply.huggingface.co>
- .gitattributes +39 -0
- README.md +11 -0
- SmolVLM-500M-Instruct-Q8_0.gguf +3 -0
- SmolVLM-500M-Instruct-f16.gguf +3 -0
- mmproj-SmolVLM-500M-Instruct-Q8_0.gguf +3 -0
- mmproj-SmolVLM-500M-Instruct-f16.gguf +3 -0
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README.md
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---
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license: apache-2.0
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base_model: HuggingFaceTB/SmolVLM-500M-Instruct
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---
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# SmolVLM-500M-Instruct
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Original model: https://huggingface.co/HuggingFaceTB/SmolVLM-500M-Instruct
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For more info, please refer to this PR: https://github.com/ggml-org/llama.cpp/pull/13050
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