Instructions to use chibifire/Qwen2.5-VL-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chibifire/Qwen2.5-VL-7B-Instruct with Transformers:

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

pipe = pipeline("image-text-to-text", model="chibifire/Qwen2.5-VL-7B-Instruct")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("chibifire/Qwen2.5-VL-7B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("chibifire/Qwen2.5-VL-7B-Instruct", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chibifire/Qwen2.5-VL-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chibifire/Qwen2.5-VL-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chibifire/Qwen2.5-VL-7B-Instruct",
		"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/chibifire/Qwen2.5-VL-7B-Instruct

SGLang

How to use chibifire/Qwen2.5-VL-7B-Instruct 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 "chibifire/Qwen2.5-VL-7B-Instruct" \
    --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": "chibifire/Qwen2.5-VL-7B-Instruct",
		"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 "chibifire/Qwen2.5-VL-7B-Instruct" \
        --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": "chibifire/Qwen2.5-VL-7B-Instruct",
		"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"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use chibifire/Qwen2.5-VL-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/chibifire/Qwen2.5-VL-7B-Instruct
```

Qwen2.5-VL-7B-Instruct

Mirror of Qwen's Qwen2.5-VL 7B instruction-tuned vision-language model, in Transformers safetensors form.

What it is for

This repo is a mirror of the instruction-tuned 7B Qwen2.5-VL model from the Qwen team. The model reads images, video and text, and writes text. It holds the sharded safetensors weights with the tokenizer, chat template, processor config and generation config. Use it as a pinned copy of the upstream weights for local inference and for conversion.

Provenance

Upstream model: Qwen/Qwen2.5-VL-7B-Instruct.
Upstream code: QwenLM/Qwen2.5-VL. Blog: Qwen2.5-VL.

Use

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor

MODEL_ID = "chibifire/Qwen2.5-VL-7B-Instruct"
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(MODEL_ID, torch_dtype="auto", device_map="auto")
processor = AutoProcessor.from_pretrained(MODEL_ID)

For image and video input, qwen_vl_utils and full examples, read the upstream model card.

Licence

Apache 2.0, the upstream licence. Terms: https://www.apache.org/licenses/LICENSE-2.0. These terms travel with the weights. Keep this link when you copy or redistribute them.

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Safetensors

Model size

8B params

Tensor type

BF16