Instructions to use HuggingFaceM4/VLM_WebSight_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/VLM_WebSight_finetuned with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceM4/VLM_WebSight_finetuned", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("HuggingFaceM4/VLM_WebSight_finetuned", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/VLM_WebSight_finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/VLM_WebSight_finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceM4/VLM_WebSight_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/VLM_WebSight_finetuned

SGLang

How to use HuggingFaceM4/VLM_WebSight_finetuned 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 "HuggingFaceM4/VLM_WebSight_finetuned" \
    --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": "HuggingFaceM4/VLM_WebSight_finetuned",
		"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 "HuggingFaceM4/VLM_WebSight_finetuned" \
        --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": "HuggingFaceM4/VLM_WebSight_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/VLM_WebSight_finetuned with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/VLM_WebSight_finetuned
```

Potential Inconsistencies Model and Datasets License

by yueyangchen - opened May 30, 2025

Discussion

yueyangchen

May 30, 2025

Hi, while reviewing the licenses for this model and datasets it depends on, I noticed a potential inconsistency that could cause confusion or legal risks in some situations.

Your model utilizes the dataset HuggingFaceM4/WebSight under the cc-by-4.0. However, the license of your model is apache-2.0, i.e., less strict than cc-by-4.0 on license terms, such as sublicense, which may impact the whole license compatibility in your repository, thus confusing subsequent users and bringing possible legal and financial risks.

If possible, you can fix them in one of the following ways:
1.It could be helpful to select another proper license for your repository.
2.You may want to gently remind users that, in some cases, they should check both the model license and the base model license, especially when redistributing or modifying the model.

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