Instructions to use OpenGVLab/InternVL-Chat-V1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL-Chat-V1-1 with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL-Chat-V1-1", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/InternVL-Chat-V1-1", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL-Chat-V1-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL-Chat-V1-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL-Chat-V1-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenGVLab/InternVL-Chat-V1-1

SGLang

How to use OpenGVLab/InternVL-Chat-V1-1 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-V1-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL-Chat-V1-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-V1-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL-Chat-V1-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenGVLab/InternVL-Chat-V1-1 with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL-Chat-V1-1
```

czczup commited on Feb 3, 2024

Commit

d1a7f0a

verified ·

1 Parent(s): f0bf9a7

Update README.md

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@@ -43,10 +43,38 @@ It is _**the largest open-source vision/vision-language foundation model (14B)**
 We will provide a minimum code example to run InternVL-Chat using only the `transformers` library.
-Before this is completed, you can use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model.
 ```python
-TODO
 ```
 ## Examples

 We will provide a minimum code example to run InternVL-Chat using only the `transformers` library.
+You also can use our [online demo](https://internvl.opengvlab.com/) for a quick experience of this model.
 ```python
+import torch
+from PIL import Image
+from transformers import AutoModel, CLIPImageProcessor
+from transformers import AutoTokenizer
+path = "OpenGVLab/InternVL-Chat-Chinese-V1-1"
+model = AutoModel.from_pretrained(
+    path,
+    torch_dtype=torch.bfloat16,
+    low_cpu_mem_usage=True,
+    trust_remote_code=True,
+    device_map='auto').eval()
+tokenizer = AutoTokenizer.from_pretrained(path)
+image = Image.open('./examples/image2.jpg').convert('RGB')
+image = image.resize((448, 448))
+image_processor = CLIPImageProcessor.from_pretrained(path)
+pixel_values = image_processor(images=image, return_tensors='pt').pixel_values
+pixel_values = pixel_values.to(torch.bfloat16).cuda()
+generation_config = dict(
+    num_beams=1,
+    max_new_tokens=512,
+    do_sample=False,
+)
+question = "请详细描述图片"
+response = model.chat(tokenizer, pixel_values, question, generation_config)
 ```
 ## Examples