Instructions to use forkjoin-ai/qwen2.5-vl-7b-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use forkjoin-ai/qwen2.5-vl-7b-instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="forkjoin-ai/qwen2.5-vl-7b-instruct-gguf",
	filename="Qwen_Qwen2.5-VL-7B-Instruct-Q4_K_M.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 forkjoin-ai/qwen2.5-vl-7b-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 forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf forkjoin-ai/qwen2.5-vl-7b-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 forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf forkjoin-ai/qwen2.5-vl-7b-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 forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf forkjoin-ai/qwen2.5-vl-7b-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 forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use forkjoin-ai/qwen2.5-vl-7b-instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "forkjoin-ai/qwen2.5-vl-7b-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": "forkjoin-ai/qwen2.5-vl-7b-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/forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M

Ollama
How to use forkjoin-ai/qwen2.5-vl-7b-instruct-gguf with Ollama:
```
ollama run hf.co/forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use forkjoin-ai/qwen2.5-vl-7b-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 forkjoin-ai/qwen2.5-vl-7b-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 forkjoin-ai/qwen2.5-vl-7b-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 forkjoin-ai/qwen2.5-vl-7b-instruct-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use forkjoin-ai/qwen2.5-vl-7b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M
```

Lemonade

How to use forkjoin-ai/qwen2.5-vl-7b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull forkjoin-ai/qwen2.5-vl-7b-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-vl-7b-instruct-gguf-Q4_K_M

List all available models

lemonade list

qwen2.5-vl-7b-instruct-gguf / README.md

buley

Fix model card accuracy (format/filename)

799b180 verified about 1 month ago

1.28 kB

language:
  - en
license: apache-2.0
library_name: llama-cpp
tags:
  - gguf
  - vision
  - multimodal
  - forkjoin-ai
base_model: Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text

Qwen2.5 Vl 7B Instruct

Forkjoin.ai conversion of Qwen/Qwen2.5-VL-7B-Instruct to GGUF format for edge deployment.

Model Details

Source Model: Qwen/Qwen2.5-VL-7B-Instruct
Format: GGUF
Converted by: Forkjoin.ai

Usage

With llama.cpp

./llama-cli -m Qwen_Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf -p "Your prompt here" -n 256

With Ollama

Create a Modelfile:

FROM ./Qwen_Qwen2.5-VL-7B-Instruct-Q4_K_M.gguf

ollama create qwen2.5-vl-7b-instruct-gguf -f Modelfile
ollama run qwen2.5-vl-7b-instruct-gguf

About Forkjoin.ai

Forkjoin.ai runs AI models at the edge -- in-browser, on-device, zero cloud cost. These converted models power real-time inference, speech recognition, and natural language capabilities.

All conversions are optimized for edge deployment within browser and mobile memory constraints.

License

Apache 2.0 (follows upstream model license)