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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use turingevo/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 turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf turingevo/Qwen2-VL-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 serve -hf turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf turingevo/Qwen2-VL-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 turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf turingevo/Qwen2-VL-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 turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use turingevo/Qwen2-VL-2B-Instruct-gguf with Ollama:
```
ollama run hf.co/turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use turingevo/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 turingevo/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 turingevo/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 turingevo/Qwen2-VL-2B-Instruct-gguf to start chatting

Docker Model Runner
How to use turingevo/Qwen2-VL-2B-Instruct-gguf with Docker Model Runner:
```
docker model run hf.co/turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M
```

Lemonade

How to use turingevo/Qwen2-VL-2B-Instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull turingevo/Qwen2-VL-2B-Instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2-VL-2B-Instruct-gguf-Q4_K_M

List all available models

lemonade list

Atomic Chat

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Check out the documentation for more information.

see help:

Follow steps:

1 Download turingevo/Qwen2-VL-2B-Instruct-gguf

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

then build and get target llama-qwen2vl-cli

3 Get pictures:

It's recommended to resize the image to a resolution below 640x640, so it won't take forever to run on CPU backend:

ffmpeg -i input.jpeg -vf "scale=512:512" 1.png

4 cmd: llama-qwen2vl-cli -m Qwen2-VL-2B-Instruct-F16.gguf --mmproj qwen2-vl-2b-instruct-vision.gguf -p "Describe this image" --image "1.png"

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GGUF

Model size

2B params

Architecture

qwen2vl

Hardware compatibility

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