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
```

ziyjiang commited on Jul 13, 2025

Commit

5813911

verified ·

1 Parent(s): 795dcd0

Update README.md

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@@ -25,8 +25,14 @@ Our model is being trained on MMEB-train and evaluated on MMEB-eval with contras
  - Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-eval
 ### VLM2Vec Checkpoints
- - [MMEB.lora8.bs1024](https://huggingface.co/TIGER-Lab/MMEB.lora8.bs1024/)
- - [MMEB.fullmodel.bs2048](https://huggingface.co/TIGER-Lab/MMEB.fullmodel.bs2048/)
 ### Github
  - [Github](https://github.com/TIGER-AI-Lab/VLM2Vec)

  - Eval data: https://huggingface.co/datasets/TIGER-Lab/MMEB-eval
 ### VLM2Vec Checkpoints
+<details>
+<summary> V1 checkpoints </summary>
+- [VLM2Vec-Qwen2VL (7B)](https://huggingface.co/TIGER-Lab/VLM2Vec-Qwen2VL-7B)
+- [VLM2Vec-Qwen2VL (2B)](https://huggingface.co/TIGER-Lab/VLM2Vec-Qwen2VL-2B)
+- [VLM2Vec-LLaVa-Next](https://huggingface.co/TIGER-Lab/VLM2Vec-LLaVa-Next)
+- [VLM2Vec-Phi3.5V](https://huggingface.co/TIGER-Lab/VLM2Vec-Full)
+</details>
 ### Github
  - [Github](https://github.com/TIGER-AI-Lab/VLM2Vec)