Instructions to use HarshKalburgi/SmolVLM-256M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HarshKalburgi/SmolVLM-256M-Instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="HarshKalburgi/SmolVLM-256M-Instruct-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HarshKalburgi/SmolVLM-256M-Instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use HarshKalburgi/SmolVLM-256M-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 HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf HarshKalburgi/SmolVLM-256M-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 HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf HarshKalburgi/SmolVLM-256M-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 HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf HarshKalburgi/SmolVLM-256M-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 HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use HarshKalburgi/SmolVLM-256M-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HarshKalburgi/SmolVLM-256M-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HarshKalburgi/SmolVLM-256M-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M

SGLang

How to use HarshKalburgi/SmolVLM-256M-Instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "HarshKalburgi/SmolVLM-256M-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HarshKalburgi/SmolVLM-256M-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "HarshKalburgi/SmolVLM-256M-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HarshKalburgi/SmolVLM-256M-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use HarshKalburgi/SmolVLM-256M-Instruct-GGUF with Ollama:
```
ollama run hf.co/HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use HarshKalburgi/SmolVLM-256M-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 HarshKalburgi/SmolVLM-256M-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 HarshKalburgi/SmolVLM-256M-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 HarshKalburgi/SmolVLM-256M-Instruct-GGUF to start chatting

Docker Model Runner
How to use HarshKalburgi/SmolVLM-256M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use HarshKalburgi/SmolVLM-256M-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SmolVLM-256M-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

SmolVLM-256M-Instruct

Model creator: HuggingFaceTB
Original model: HuggingFaceTB/SmolVLM-256M-Instruct
GGUF quantization: provided by HarshKalburgi using llama.cpp

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

Use with Ollama

ollama run "hf.co/HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M"

Use with LM Studio

lms load "HarshKalburgi/SmolVLM-256M-Instruct-GGUF"

Use with llama.cpp CLI

llama-cli --hf "HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M" -p "The meaning to life and the universe is"

Use with llama.cpp Server:

llama-server --hf "HarshKalburgi/SmolVLM-256M-Instruct-GGUF:Q4_K_M" -c 4096

Downloads last month: 39

GGUF

Model size

0.2B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for HarshKalburgi/SmolVLM-256M-Instruct-GGUF

Base model

HuggingFaceTB/SmolLM2-135M

Quantized

HuggingFaceTB/SmolLM2-135M-Instruct

Quantized

HuggingFaceTB/SmolVLM-256M-Instruct

Quantized

(21)

this model

HarshKalburgi
/

SmolVLM-256M-Instruct-GGUF

SmolVLM-256M-Instruct

Special thanks

Use with Ollama

Use with LM Studio

Use with llama.cpp CLI

Use with llama.cpp Server:

Model tree for HarshKalburgi/SmolVLM-256M-Instruct-GGUF

Datasets used to train HarshKalburgi/SmolVLM-256M-Instruct-GGUF