Instructions to use sidrajaram/Qwen2-VL-2B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sidrajaram/Qwen2-VL-2B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sidrajaram/Qwen2-VL-2B-Instruct-GGUF",
	filename="Qwen2-VL-2B-Instruct-F16.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sidrajaram/Qwen2-VL-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 sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16

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 sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16

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 sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16

Use Docker

docker model run hf.co/sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16

LM Studio
Jan

vLLM

How to use sidrajaram/Qwen2-VL-2B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sidrajaram/Qwen2-VL-2B-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": "sidrajaram/Qwen2-VL-2B-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/sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16

Ollama
How to use sidrajaram/Qwen2-VL-2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16
```

Unsloth Studio

How to use sidrajaram/Qwen2-VL-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 sidrajaram/Qwen2-VL-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 sidrajaram/Qwen2-VL-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 sidrajaram/Qwen2-VL-2B-Instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use sidrajaram/Qwen2-VL-2B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16
```

Lemonade

How to use sidrajaram/Qwen2-VL-2B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sidrajaram/Qwen2-VL-2B-Instruct-GGUF:F16

Run and chat with the model

lemonade run user.Qwen2-VL-2B-Instruct-GGUF-F16

List all available models

lemonade list

Qwen2-VL-2B-Instruct-GGUF (f16)

This is a F16 GGUF version of Qwen2-VL-2B-Instruct for use with llama.cpp (i.e. you can run Qwen2-VL on your Mac)

How to Use

Build llama-qwen2vl-cli executable
Download model files and use: ./llama-qwen2vl-cli -m Qwen2-VL-2B-Instruct-F16.gguf --mmproj qwen2-vl-2b-instruct-vision.gguf -p "Describe this image." --image crocodiles.png

Details on Usage:

Download the model files from this repository (sidrajaram/Qwen2-VL-2B-Instruct-GGUF).
Make sure you have llama.cpp and have built the llama-qwen2vl-cli executable.

git clone https://github.com/ggerganov/llama.cpp.git

For example, building with CMake (see detailed llama.cpp build instructions: https://github.com/ggerganov/llama.cpp/blob/master/docs/build.md):

cmake -B build
cmake --build build --config Release

Run

./path/to/llama-qwen2vl-cli -m path/to/Qwen2-VL-2B-Instruct-F16.gguf --mmproj path/to/qwen2-vl-2b-instruct-vision.gguf -p "Describe this image." --image path/to/image.png

Note: According to llama.cpp contributors, "it's recommended to resize the image to a resolution below 640x640, so it won't take forever to run on CPU backend"

Credit to the original model: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct

Downloads last month: 68

GGUF

Model size

2B params

Architecture

qwen2vl

Hardware compatibility

16-bit

View +1 variant

Model tree for sidrajaram/Qwen2-VL-2B-Instruct-GGUF

Base model

Qwen/Qwen2-VL-2B

Finetuned

Qwen/Qwen2-VL-2B-Instruct

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(55)

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