Image-to-Text
Transformers
Safetensors
phi3_v
text-generation
chart-captioning
multimodal
vision-language-model
custom_code
Instructions to use junyoung-00/Phi-3.5-vision-instruct-ChartCap with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use junyoung-00/Phi-3.5-vision-instruct-ChartCap with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("image-to-text", model="junyoung-00/Phi-3.5-vision-instruct-ChartCap", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("junyoung-00/Phi-3.5-vision-instruct-ChartCap", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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This repository contains the model presented in the paper [**ChartCap: Mitigating Hallucination of Dense Chart Captioning**](https://huggingface.co/papers/2508.03164).
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**Project Page:** [https://junyoung-00.github.io/ChartCap/](https://junyoung-00.github.io/ChartCap/)\
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**Code:**
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## Model Description
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This repository contains the model presented in the paper [**ChartCap: Mitigating Hallucination of Dense Chart Captioning**](https://huggingface.co/papers/2508.03164).
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**Project Page:** [https://junyoung-00.github.io/ChartCap/](https://junyoung-00.github.io/ChartCap/)\
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**Code:** [https://github.com/junyoung-00/ChartCap](https://github.com/junyoung-00/ChartCap)
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## Model Description
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