Instructions to use OpenGVLab/InternVL2-2B-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenGVLab/InternVL2-2B-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="OpenGVLab/InternVL2-2B-AWQ", 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/InternVL2-2B-AWQ", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenGVLab/InternVL2-2B-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenGVLab/InternVL2-2B-AWQ"
# 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/InternVL2-2B-AWQ",
		"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/InternVL2-2B-AWQ

SGLang

How to use OpenGVLab/InternVL2-2B-AWQ 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/InternVL2-2B-AWQ" \
    --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/InternVL2-2B-AWQ",
		"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/InternVL2-2B-AWQ" \
        --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/InternVL2-2B-AWQ",
		"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/InternVL2-2B-AWQ with Docker Model Runner:
```
docker model run hf.co/OpenGVLab/InternVL2-2B-AWQ
```

czczup commited on Nov 21, 2024

Commit

5138327

verified ·

1 Parent(s): da16787

fix compatibility issue for transformers 4.46+

Browse files

Files changed (2) hide show

configuration_internvl_chat.py +3 -3
conversation.py +1 -1

configuration_internvl_chat.py CHANGED Viewed

@@ -47,12 +47,12 @@ class InternVLChatConfig(PretrainedConfig):
             logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
         self.vision_config = InternVisionConfig(**vision_config)
-        if llm_config.get('architectures', ['Qwen2ForCausalLM'])[0] == 'LlamaForCausalLM':
             self.llm_config = LlamaConfig(**llm_config)
-        elif llm_config.get('architectures', ['InternLM2ForCausalLM'])[0] == 'InternLM2ForCausalLM':
             self.llm_config = InternLM2Config(**llm_config)
         else:
-            raise ValueError('Unsupported architecture: {}'.format(llm_config['architectures'][0]))
         self.use_backbone_lora = use_backbone_lora
         self.use_llm_lora = use_llm_lora
         self.select_layer = select_layer

             logger.info('llm_config is None. Initializing the LlamaConfig config with default values (`LlamaConfig`).')
         self.vision_config = InternVisionConfig(**vision_config)
+        if llm_config.get('architectures')[0] == 'LlamaForCausalLM':
             self.llm_config = LlamaConfig(**llm_config)
+        elif llm_config.get('architectures')[0] == 'InternLM2ForCausalLM':
             self.llm_config = InternLM2Config(**llm_config)
         else:
+            raise ValueError('Unsupported architecture: {}'.format(llm_config.get(['architectures'])[0]))
         self.use_backbone_lora = use_backbone_lora
         self.use_llm_lora = use_llm_lora
         self.select_layer = select_layer

conversation.py CHANGED Viewed

@@ -9,7 +9,7 @@ Modified from https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation
 import dataclasses
 from enum import IntEnum, auto
-from typing import Any, Dict, List, Tuple, Union
 class SeparatorStyle(IntEnum):

 import dataclasses
 from enum import IntEnum, auto
+from typing import Dict, List, Tuple, Union
 class SeparatorStyle(IntEnum):