Visual Question Answering
Transformers
Safetensors
Chinese
English
QH_360VL
text-generation
custom_code
Instructions to use ecfirst/360VL_PHI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ecfirst/360VL_PHI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="ecfirst/360VL_PHI", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ecfirst/360VL_PHI", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
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<img src="https://github.com/360CVGroup/360VL/blob/master/qh360_vl/360vl.PNG?raw=true" width=100%/>
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**360VL** is developed based on the LLama3 language model and is also the industry's first open source multi-modal
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## Model Zoo
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<img src="https://github.com/360CVGroup/360VL/blob/master/qh360_vl/360vl.PNG?raw=true" width=100%/>
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**360VL** is developed based on the LLama3 language model and is also the industry's first open source large multi-modal model based on **LLama3-70B**[[🤗Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct)]. In addition to applying the Llama3 language model, the 360VL model also designs a globally aware multi-branch projector architecture, which enables the model to have more sufficient image understanding capabilities.
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## Model Zoo
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