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

llama.cpp

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

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

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

SmolVLM2-2.2B-Instruct GGUF

GGUF quantizations of 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

# 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

# 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|>

Credits

Original Model: HuggingFace
Quantization: Richard Young (deepneuro.ai)

License

Apache 2.0

Downloads last month: 35

GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

View +1 variant

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for richardyoung/SmolVLM2-2.2B-Instruct-GGUF

Base model

HuggingFaceTB/SmolLM2-1.7B

Quantized

HuggingFaceTB/SmolLM2-1.7B-Instruct

Quantized

HuggingFaceTB/SmolVLM-Instruct

Finetuned

HuggingFaceTB/SmolVLM2-2.2B-Instruct

Quantized

(11)

this model

richardyoung
/

SmolVLM2-2.2B-Instruct-GGUF

SmolVLM2-2.2B-Instruct GGUF

Model Description

Key Features

Available Quantizations

Usage

With Ollama

With llama.cpp

Technical Requirements

Chat Template

Links

Credits

License

Model tree for richardyoung/SmolVLM2-2.2B-Instruct-GGUF