Instructions to use Zuyan/llava-CoS-13B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Zuyan/llava-CoS-13B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zuyan/llava-CoS-13B")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("Zuyan/llava-CoS-13B") model = AutoModelForCausalLM.from_pretrained("Zuyan/llava-CoS-13B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Zuyan/llava-CoS-13B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Zuyan/llava-CoS-13B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Zuyan/llava-CoS-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Zuyan/llava-CoS-13B
- SGLang
How to use Zuyan/llava-CoS-13B 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 "Zuyan/llava-CoS-13B" \ --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": "Zuyan/llava-CoS-13B", "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 "Zuyan/llava-CoS-13B" \ --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": "Zuyan/llava-CoS-13B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Zuyan/llava-CoS-13B with Docker Model Runner:
docker model run hf.co/Zuyan/llava-CoS-13B
Create README.md
Browse files
README.md
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<p align="center" width="100%">
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<img src="https://ice.frostsky.com/2024/03/17/214a3af4a34a26be0a04e551e16b9364.webp" width="40%" height="80%">
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</p>
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# Chain-of-Spot: Interactive Reasoning Improves Large Vision-Language Models
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### Model details:
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Chain-of-Spot encourages Large Vision-Language Models to identify the region of interest (ROI) in the image condition on the question and reasoning through an interactive manner, thereby improving the ability of visual understanding.
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### Where to send questions or comments about the model: https://github.com/dongyh20/Chain-of-Spot
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### Paper or resources for more information: https://sites.google.com/view/chain-of-spot/
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