Instructions to use OpenGVLab/InternVL-Chat-V1-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL-Chat-V1-5 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL-Chat-V1-5", trust_remote_code=True)
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)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/InternVL-Chat-V1-5", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL-Chat-V1-5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL-Chat-V1-5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL-Chat-V1-5",
		"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/OpenGVLab/InternVL-Chat-V1-5

SGLang

How to use OpenGVLab/InternVL-Chat-V1-5 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 "OpenGVLab/InternVL-Chat-V1-5" \
    --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": "OpenGVLab/InternVL-Chat-V1-5",
		"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 "OpenGVLab/InternVL-Chat-V1-5" \
        --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": "OpenGVLab/InternVL-Chat-V1-5",
		"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 OpenGVLab/InternVL-Chat-V1-5 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-5
```

Quantized model coming?

by dnhkng - opened Apr 21, 2024

Discussion

dnhkng

Apr 21, 2024

Do you plan to release a quantized version, or do you have tips on the best way to quantize this model for local inference?

failspy

Apr 22, 2024

Hi, I just uploaded two quantized versions: an 8bit and a 4bit, using BitsAndBytes conversions. Would be curious how it works for you!

ceoofcapybaras

Apr 23, 2024

•

edited Apr 23, 2024

A GGUF would be nice for people who do not have nvidia gpus.
UPD: oh, it is not yet supported https://github.com/ggerganov/llama.cpp/issues/6803

czczup

OpenGVLab org Apr 25, 2024

Thank you for your attention. We plan to release the quantized model in the next few days. We have successfully implemented 8-bit quantization, but encountered some issues with 4-bit.

whai362

Apr 28, 2024

https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5-Int8

nofreewill

May 15, 2024

Is it in plan to look into the issues with the 4-bit version?

nofreewill

May 15, 2024

•

edited May 15, 2024

https://huggingface.co/failspy/InternVL-Chat-V1-5-4bit
This fits on my 3090 but the response is only an empty string "" to such an image with the following prompt (question)

question = "Please describe the picture in detail"
response = model.chat(tokenizer, pixel_values, question, generation_config)
print(question, response)

I was following https://huggingface.co/OpenGVLab/InternVL-Chat-V1-5#model-usage , just switched path = "OpenGVLab/InternVL-Chat-V1-5" to path = "failspy/InternVL-Chat-V1-5-4bit" and load_in_4bit instead of load_in_8bit, but got "" as response from model.:( But at least I hope that this means I will be able to run a working one, I hope so because the online demo has impressed me big time!

ubergarm

May 17, 2024

@nofreewill

I have the same issue with the failspy/InternVL-Chat-V1-5-4bit on my 3090TI... Possibly something with the quantization_config in the config.json possibly? I'm curious if anyone has actually run this locally in 24GB VRAM...

v3ss0n

Jul 13, 2024

I tried it locally with 22GB VRAM Nvidia A10G with AQM Quant , not enough memory. How much total needed for 4 bit q?

czczup changed discussion status to closed Jul 29, 2024

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