Instructions to use SenseLLM/SpiritSight-Agent-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SenseLLM/SpiritSight-Agent-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="SenseLLM/SpiritSight-Agent-8B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SenseLLM/SpiritSight-Agent-8B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SenseLLM/SpiritSight-Agent-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SenseLLM/SpiritSight-Agent-8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SenseLLM/SpiritSight-Agent-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SenseLLM/SpiritSight-Agent-8B
- SGLang
How to use SenseLLM/SpiritSight-Agent-8B 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 "SenseLLM/SpiritSight-Agent-8B" \ --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": "SenseLLM/SpiritSight-Agent-8B", "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 "SenseLLM/SpiritSight-Agent-8B" \ --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": "SenseLLM/SpiritSight-Agent-8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SenseLLM/SpiritSight-Agent-8B with Docker Model Runner:
docker model run hf.co/SenseLLM/SpiritSight-Agent-8B
Is there a demo for desktop?
Hey, I noticed the infer demo under infer_SSAgent-8B.py is designed for mobile app, do you have any examples for the desktop apps(web browser...)
Thank you for your interest in our work.
To perform inference in a desktop scenario, you need to:
a) Update the task description;
b) Replace the image file with a desktop screenshot;
c) Update the definition of the action space;
d) If it's not the first step, you need to supplement the historical actions.
Currently, the demo is only for single-step inference. We will roll out an automated inference framework in the future.
Thank you for your interest in our work.
To perform inference in a desktop scenario, you need to:
a) Update the task description;
b) Replace the image file with a desktop screenshot;
c) Update the definition of the action space;
d) If it's not the first step, you need to supplement the historical actions.Currently, the demo is only for single-step inference. We will roll out an automated inference framework in the future.
Thx!
I would like to make sure: is this model robust to a custom action space? That is, no necessary to follow the names and parameters that were used during training? If not, could you please provide the recommend action space for desktop?
Sure. Here is an action space for the Mind2Web dataset for your reference:
- CLICK([block_index, cx, cy, w, h], "text")
- SELECT([block_index, cx, cy, w, h], "text")
- TYPE("text")