GraySoft
Projects Models Compare Cloud benchmarks FAQ Download guIDE →
Model Intelligence Sheet

richardyoung/SmolVLM2-2.2B-Instruct-GGUF overview

SmolVLM2 2.2B Instruct GGUF GGUF quantizations of HuggingFaceTB/SmolVLM2 2.2B Instruct https://huggingface.co/HuggingFaceTB/SmolVLM2 2.2B Instruct for use with…

transformersggufvisionmultimodalllama-cppquantizedenbase_model:HuggingFaceTB/SmolVLM2-2.2B-Instructbase_model:quantized:HuggingFaceTB/SmolVLM2-2.2B-Instructlicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~1.04 GB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

Downloads
80
Likes
0
Pipeline
—

Repository Files & Downloads

3 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
SmolVLM2-2.2B-Instruct-Q4_K_M.ggufGGUFQ4_K_M1.04 GBDownload
SmolVLM2-2.2B-Instruct-Q8_0.ggufGGUFQ8_01.80 GBDownload
SmolVLM2-2.2B-Instruct.ggufGGUFGGUF3.38 GBDownload

Model Details

Model IDrichardyoung/SmolVLM2-2.2B-Instruct-GGUF
Authorrichardyoung
Pipeline—
Licenseapache-2.0
Base modelHuggingFaceTB/SmolVLM2-2.2B-Instruct
Last modified2026-09-26T19:09:47.000Z

Model README

---

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 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|>

Links

Credits

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

License

Apache 2.0

---

Built & maintained by Richard Young · DeepNeuro

Run richardyoung/SmolVLM2-2.2B-Instruct-GGUF with guIDE

Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.

Download guIDE → · Browse 524k+ models · Compare models

Source: Hugging Face · Compare models