Instructions to use richardyoung/SmolVLM2-2.2B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use richardyoung/SmolVLM2-2.2B-Instruct-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("richardyoung/SmolVLM2-2.2B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use richardyoung/SmolVLM2-2.2B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="richardyoung/SmolVLM2-2.2B-Instruct-GGUF",
	filename="SmolVLM2-2.2B-Instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use richardyoung/SmolVLM2-2.2B-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 richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf richardyoung/SmolVLM2-2.2B-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 richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf richardyoung/SmolVLM2-2.2B-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 richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf richardyoung/SmolVLM2-2.2B-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 richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use richardyoung/SmolVLM2-2.2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use richardyoung/SmolVLM2-2.2B-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 richardyoung/SmolVLM2-2.2B-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 richardyoung/SmolVLM2-2.2B-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 richardyoung/SmolVLM2-2.2B-Instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use richardyoung/SmolVLM2-2.2B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use richardyoung/SmolVLM2-2.2B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull richardyoung/SmolVLM2-2.2B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolVLM2-2.2B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

richardyoung commited on Nov 26, 2025

Commit

b70a33a

verified ·

1 Parent(s): b264541

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+base_model: HuggingFaceTB/SmolVLM2-2.2B-Instruct
+tags:
+- vision
+- multimodal
+- llama-cpp
+- gguf
+- quantized
+---
+# SmolVLM2-2.2B-Instruct GGUF
+GGUF quantizations of [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) for use with llama.cpp and Ollama.
+## Model Description
+SmolVLM2 is a compact 2.2B parameter vision-language model from HuggingFace with video understanding capabilities. It's designed to be fast and efficient while maintaining strong performance on vision-language tasks.
+## Key Features
+- **Compact & Fast** - Only 2.2B parameters, runs efficiently on consumer hardware
+- **Vision & Video** - Understands both images and video frames
+- **Instruction-tuned** - Optimized for following user instructions
+- **Apache 2.0** - Fully open source
+## Available Quantizations
+| Filename | Quant | Size | Description |
+|----------|-------|------|-------------|
+| SmolVLM2-2.2B-Instruct-Q4_K_M.gguf | Q4_K_M | 1.0 GB | Best balance of quality and speed (recommended) |
+| SmolVLM2-2.2B-Instruct-Q8_0.gguf | Q8_0 | 1.8 GB | Higher quality |
+| SmolVLM2-2.2B-Instruct.gguf | F16 | 3.4 GB | Full precision |
+## Usage
+### With Ollama
+```bash
+# Pull and run (Q4_K_M by default)
+ollama run richardyoung/smolvlm2-2.2b-instruct
+# Or specific quantization
+ollama run richardyoung/smolvlm2-2.2b-instruct:q8_0
+ollama run richardyoung/smolvlm2-2.2b-instruct:f16
+```
+### With llama.cpp
+```bash
+# Download a quantization
+wget https://huggingface.co/richardyoung/SmolVLM2-2.2B-Instruct-GGUF/resolve/main/SmolVLM2-2.2B-Instruct-Q4_K_M.gguf
+# Run with llama.cpp
+./llama-cli -m SmolVLM2-2.2B-Instruct-Q4_K_M.gguf -p "Describe this image:" --image your_image.jpg
+```
+## Technical Requirements
+- **Minimum:** 4GB RAM, any modern CPU
+- **Recommended:** 8GB RAM or Apple Silicon Mac
+## Chat Template
+SmolVLM2 uses the ChatML format:
+```
+<|im_start|>system
+{system_message}<|im_end|>
+<|im_start|>user
+{user_message}<|im_end|>
+<|im_start|>assistant
+{assistant_response}<|im_end|>
+```
+## Links
+- **Original Model:** [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct)
+- **Ollama:** [richardyoung/smolvlm2-2.2b-instruct](https://ollama.com/richardyoung/smolvlm2-2.2b-instruct)
+## Credits
+- **Original Model:** HuggingFace
+- **Quantization:** Richard Young ([deepneuro.ai](https://deepneuro.ai/richard))
+## License
+Apache 2.0