Instructions to use TIGER-Lab/VLM2Vec-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TIGER-Lab/VLM2Vec-LoRA with Transformers:

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

pipe = pipeline("text-generation", model="TIGER-Lab/VLM2Vec-LoRA", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("TIGER-Lab/VLM2Vec-LoRA", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TIGER-Lab/VLM2Vec-LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TIGER-Lab/VLM2Vec-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TIGER-Lab/VLM2Vec-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TIGER-Lab/VLM2Vec-LoRA

SGLang

How to use TIGER-Lab/VLM2Vec-LoRA 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 "TIGER-Lab/VLM2Vec-LoRA" \
    --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": "TIGER-Lab/VLM2Vec-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "TIGER-Lab/VLM2Vec-LoRA" \
        --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": "TIGER-Lab/VLM2Vec-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TIGER-Lab/VLM2Vec-LoRA with Docker Model Runner:
```
docker model run hf.co/TIGER-Lab/VLM2Vec-LoRA
```

wenhu commited on Nov 2, 2024

Commit

db76f1d

verified ·

1 Parent(s): 10b6452

Update README.md

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README.md +13 -4

README.md CHANGED Viewed

@@ -53,11 +53,9 @@ from PIL import Image
 import numpy as np
 model_args = ModelArguments(
-    model_name='microsoft/Phi-3.5-vision-instruct',
     pooling='last',
-    normalize=True,
-    lora=True,
-    checkpoint_path='TIGER-Lab/VLM2Vec-LoRA')
 model = MMEBModel.load(model_args)
 model.eval()
@@ -99,6 +97,17 @@ inputs = {key: value.to('cuda') for key, value in inputs.items()}
 tgt_output = model(tgt=inputs)["tgt_reps"]
 print(string, '=', model.compute_similarity(qry_output, tgt_output))
 ## <|image_1|> Represent the given image. = tensor([[0.3105]], device='cuda:0', dtype=torch.bfloat16)
 ```
 ## Citation

 import numpy as np
 model_args = ModelArguments(
+    model_name='TIGER-Lab/VLM2Vec-Full',
     pooling='last',
+    normalize=True)
 model = MMEBModel.load(model_args)
 model.eval()
 tgt_output = model(tgt=inputs)["tgt_reps"]
 print(string, '=', model.compute_similarity(qry_output, tgt_output))
 ## <|image_1|> Represent the given image. = tensor([[0.3105]], device='cuda:0', dtype=torch.bfloat16)
+inputs = processor('Find me an everyday image that matches the given caption: A cat and a tiger.',)
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+qry_output = model(qry=inputs)["qry_reps"]
+string = '<|image_1|> Represent the given image.'
+inputs = processor(string, [Image.open('figures/example.jpg')])
+inputs = {key: value.to('cuda') for key, value in inputs.items()}
+tgt_output = model(tgt=inputs)["tgt_reps"]
+print(string, '=', model.compute_similarity(qry_output, tgt_output))
+## <|image_1|> Represent the given image. = tensor([[0.2158]], device='cuda:0', dtype=torch.bfloat16)
 ```
 ## Citation