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

Libraries

How to use cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF",
	filename="Qwen2-VL-2B-ReverseImagePrompter-IQ4_NL.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-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 cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-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 cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF with Ollama:
```
ollama run hf.co/cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M
```

Unsloth Studio new

How to use cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-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 cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-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 cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF to start chatting

Docker Model Runner
How to use cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M
```

Lemonade

How to use cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF-Q4_K_M

List all available models

lemonade list

Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF

Original model: Qwen2-VL-2B-ReverseImagePrompter
Creator: trollek

Quantization notes

Made with llama.cpp-b4608 with imatrix based on Exllamav2 calibration data.
When I tested these quants, latest llama.cpp was locked to use CPU backend for vision models.
Hopefully it gets fixed sooner than later.

Original model card

Reverse Image Prompts with ease

System: You describe images by the image generation prompt that could have created them.

Prompts:

Describe this image in a single sentence.
Describe this image in detail.
Describe this image thoroughly and in great detail.
Describe this image using danbooru keywords.
Describe this image in great detail followed by danbooru keywords.

Downloads last month: 28

GGUF

Model size

2B params

Architecture

qwen2vl

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for cgus/Qwen2-VL-2B-ReverseImagePrompter-iMat-GGUF

Base model

Qwen/Qwen2-VL-2B

Finetuned

Qwen/Qwen2-VL-2B-Instruct

Finetuned

trollek/Qwen2-VL-2B-ReverseImagePrompter

Quantized

(1)

this model