SmolVLM real-time camera demo

This repository is a simple demo for how to use llama.cpp server with SmolVLM 500M to get real-time object detection

How to setup

1. Install llama.cpp
2. Run llama-server -hf ggml-org/SmolVLM-500M-Instruct-GGUF
   Note: you may need to add -ngl 99 to enable GPU (if you are using NVidia/AMD/Intel GPU)
   Note (2): You can also try other models here
3. Open index.html
4. Optionally change the instruction (for example, make it returns JSON)
5. Click on "Start" and enjoy

Multimodal

llama.cpp supports multimodal input via libmtmd. Currently, there are 2 tools support this feature:

• llama-mtmd-cli
• llama-server via OpenAI-compatible /chat/completions API

Currently, we support image and audio input. Audio is highly experimental and may have reduced quality.

To enable it, you can use one of the 2 methods below:

• Use -hf option with a supported model (see a list of pre-quantized model below)
  • To load a model using -hf while disabling multimodal, use --no-mmproj
  • To load a model using -hf while using a custom mmproj file, use --mmproj local_file.gguf
• Use -m model.gguf option with --mmproj file.gguf to specify text and multimodal projector respectively

By default, multimodal projector will be offloaded to GPU. To disable this, add --no-mmproj-offload

For example:

# simple usage with CLI
llama-mtmd-cli -hf ggml-org/gemma-3-4b-it-GGUF

# simple usage with server
llama-server -hf ggml-org/gemma-3-4b-it-GGUF

# using local file
llama-server -m gemma-3-4b-it-Q4_K_M.gguf --mmproj mmproj-gemma-3-4b-it-Q4_K_M.gguf

# no GPU offload
llama-server -hf ggml-org/gemma-3-4b-it-GGUF --no-mmproj-offload

Pre-quantized models

These are ready-to-use models, most of them come with Q4_K_M quantization by default. They can be found at the Hugging Face page of the ggml-org: https://huggingface.co/ggml-org

Replaces the (tool_name) with the name of binary you want to use. For example, llama-mtmd-cli or llama-server

NOTE: some models may require large context window, for example: -c 8192

Vision models:

# Gemma 3
(tool_name) -hf ggml-org/gemma-3-4b-it-GGUF
(tool_name) -hf ggml-org/gemma-3-12b-it-GGUF
(tool_name) -hf ggml-org/gemma-3-27b-it-GGUF

# SmolVLM
(tool_name) -hf ggml-org/SmolVLM-Instruct-GGUF
(tool_name) -hf ggml-org/SmolVLM-256M-Instruct-GGUF
(tool_name) -hf ggml-org/SmolVLM-500M-Instruct-GGUF
(tool_name) -hf ggml-org/SmolVLM2-2.2B-Instruct-GGUF
(tool_name) -hf ggml-org/SmolVLM2-256M-Video-Instruct-GGUF
(tool_name) -hf ggml-org/SmolVLM2-500M-Video-Instruct-GGUF

# Pixtral 12B
(tool_name) -hf ggml-org/pixtral-12b-GGUF

# Qwen 2 VL
(tool_name) -hf ggml-org/Qwen2-VL-2B-Instruct-GGUF
(tool_name) -hf ggml-org/Qwen2-VL-7B-Instruct-GGUF

# Qwen 2.5 VL
(tool_name) -hf ggml-org/Qwen2.5-VL-3B-Instruct-GGUF
(tool_name) -hf ggml-org/Qwen2.5-VL-7B-Instruct-GGUF
(tool_name) -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF
(tool_name) -hf ggml-org/Qwen2.5-VL-72B-Instruct-GGUF

# Mistral Small 3.1 24B (IQ2_M quantization)
(tool_name) -hf ggml-org/Mistral-Small-3.1-24B-Instruct-2503-GGUF

# InternVL 2.5 and 3
(tool_name) -hf ggml-org/InternVL2_5-1B-GGUF
(tool_name) -hf ggml-org/InternVL2_5-4B-GGUF
(tool_name) -hf ggml-org/InternVL3-1B-Instruct-GGUF
(tool_name) -hf ggml-org/InternVL3-2B-Instruct-GGUF
(tool_name) -hf ggml-org/InternVL3-8B-Instruct-GGUF
(tool_name) -hf ggml-org/InternVL3-14B-Instruct-GGUF

# Llama 4 Scout
(tool_name) -hf ggml-org/Llama-4-Scout-17B-16E-Instruct-GGUF

Audio models:

# Ultravox 0.5
(tool_name) -hf ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF
(tool_name) -hf ggml-org/ultravox-v0_5-llama-3_1-8b-GGUF