Instructions to use OpenGVLab/InternVL2-Pretrain-Models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-Pretrain-Models with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-Pretrain-Models", trust_remote_code=True)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("OpenGVLab/InternVL2-Pretrain-Models", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL2-Pretrain-Models with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-Pretrain-Models"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenGVLab/InternVL2-Pretrain-Models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenGVLab/InternVL2-Pretrain-Models

SGLang

How to use OpenGVLab/InternVL2-Pretrain-Models 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/InternVL2-Pretrain-Models" \
    --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/InternVL2-Pretrain-Models",
		"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/InternVL2-Pretrain-Models" \
        --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/InternVL2-Pretrain-Models",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenGVLab/InternVL2-Pretrain-Models with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-Pretrain-Models
```

InternVL2-Pretrain-Models / InternVL2-8B-Pretrain /special_tokens_map.json

czczup

Release InternVL 2.0 pretrained models

186b555 verified over 1 year ago

raw

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1.98 kB

	{
	"additional_special_tokens": [
	"<\|im_start\|>",
	"<\|im_end\|>",
	"<\|action_start\|>",
	"<\|action_end\|>",
	"<\|interpreter\|>",
	"<\|plugin\|>",
	{
	"content": "<img>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "</img>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	{
	"content": "<IMG_CONTEXT>",
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	{
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	},
	{
	"content": "<box>",
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	{
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	"lstrip": false,
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	],
	"bos_token": {
	"content": "<s>",
	"lstrip": false,
	"normalized": false,
	"rstrip": false,
	"single_word": false
	},
	"eos_token": {
	"content": "</s>",
	"lstrip": false,
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	"pad_token": {
	"content": "</s>",
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	"unk_token": {
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	"lstrip": false,
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	"rstrip": false,
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	}
	}