Instructions to use OpenGVLab/InternVL-Chat-V1-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL-Chat-V1-2 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL-Chat-V1-2", 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-V1-2", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL-Chat-V1-2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL-Chat-V1-2"
# 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-V1-2",
		"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-V1-2

SGLang

How to use OpenGVLab/InternVL-Chat-V1-2 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-V1-2" \
    --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-V1-2",
		"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-V1-2" \
        --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-V1-2",
		"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-V1-2 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-2
```

czczup commited on Feb 5, 2025

Commit

b4d9a57

verified ·

1 Parent(s): de73580

Fix device_map

Browse files

Files changed (3) hide show

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README.md +1 -0
examples/image2.jpg +0 -0

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@@ -16,4 +16,5 @@ model-00015-of-00017.safetensors filter=lfs diff=lfs merge=lfs -text
 model-00016-of-00017.safetensors filter=lfs diff=lfs merge=lfs -text
 model-00017-of-00017.safetensors filter=lfs diff=lfs merge=lfs -text
 tokenizer.model filter=lfs diff=lfs merge=lfs -text
 examples/red-panda.mp4 filter=lfs diff=lfs merge=lfs -text

 model-00016-of-00017.safetensors filter=lfs diff=lfs merge=lfs -text
 model-00017-of-00017.safetensors filter=lfs diff=lfs merge=lfs -text
 tokenizer.model filter=lfs diff=lfs merge=lfs -text
+examples/image2.jpg filter=lfs diff=lfs merge=lfs -text
 examples/red-panda.mp4 filter=lfs diff=lfs merge=lfs -text

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@@ -164,6 +164,7 @@ def split_model(model_name):
     device_map['language_model.model.embed_tokens'] = 0
     device_map['language_model.output'] = 0
     device_map['language_model.model.norm'] = 0
     device_map['language_model.lm_head'] = 0
     device_map[f'language_model.model.layers.{num_layers - 1}'] = 0

     device_map['language_model.model.embed_tokens'] = 0
     device_map['language_model.output'] = 0
     device_map['language_model.model.norm'] = 0
+    device_map['language_model.model.rotary_emb'] = 0
     device_map['language_model.lm_head'] = 0
     device_map[f'language_model.model.layers.{num_layers - 1}'] = 0

examples/image2.jpg CHANGED Viewed

Git LFS Details

SHA256: 08487494b8dc08d44bc36491adf3ab89ff30d13a3122da86f3cd67cad89eeee8
Pointer size: 131 Bytes
Size of remote file: 126 kB