Image-Text-to-Text
Transformers
Safetensors
qwen2_5_vl
vision
web-agents
browser-automation
websight
text-generation-inference
Instructions to use tanvirb/websight-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tanvirb/websight-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="tanvirb/websight-7B")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("tanvirb/websight-7B") model = AutoModelForImageTextToText.from_pretrained("tanvirb/websight-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use tanvirb/websight-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tanvirb/websight-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tanvirb/websight-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tanvirb/websight-7B
- SGLang
How to use tanvirb/websight-7B 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 "tanvirb/websight-7B" \ --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": "tanvirb/websight-7B", "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 "tanvirb/websight-7B" \ --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": "tanvirb/websight-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tanvirb/websight-7B with Docker Model Runner:
docker model run hf.co/tanvirb/websight-7B
Websight-7B (Merged)
This is a merged version of the Websight-7B model, ready for deployment and inference.
Model Details
- Base Model: ByteDance-Seed/UI-TARS-1.5-7B
- Source PEFT Model: Asanshay/websight-7B (previous model saved here)
- Model Type: Vision-Language Model for Web Agent Tasks
- License: Apache 2.0
Usage
from transformers import pipeline
# Load the model
pipe = pipeline("image-text-to-text", model="tanvirb/websight-7B")
# Use for web agent tasks
result = pipe(text="Click the login button", images=[screenshot])
Deployment
This model is ready for:
- Hugging Face Inference Endpoints
- Local inference
- Integration with web automation pipelines
Training
This model was fine-tuned using PEFT (Parameter Efficient Fine-Tuning) techniques on web interaction data.
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Base model
ByteDance-Seed/UI-TARS-1.5-7B