Instructions to use DataCanvas/MMAlaya with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DataCanvas/MMAlaya 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="DataCanvas/MMAlaya", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DataCanvas/MMAlaya", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload README.md
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README.md
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license: apache-2.0
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pipeline_tag: image-to-text
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# MMAlaya
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MMAlaya是基于大语言模型[Alaya](https://github.com/DataCanvasIO/Alaya)的多模态模型。
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MMAlaya包含以下三个模块:
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<br>1,大语言模型Alaya。
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<br>2,图像文本特征编码器[blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b)
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<br>3,图像文本特征到大预言模型的线性投影器。
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模型的训练主要基于[LLaVA](https://github.com/haotian-liu/LLaVA)架构
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2024.01.23 最终在[MMBench](https://mmbench.opencompass.org.cn)线上测试中文测试集分数为56.9,英文测试集分数为59.8。
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推理可以参考 [inference.py](https://github.com/bingwork/MMAlaya/blob/inference/inference.py)
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# MMAlaya
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MMAlaya是基于大语言模型[Alaya](https://github.com/DataCanvasIO/Alaya)的多模态模型。
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MMAlaya包含以下三个模块:
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<br>1,大语言模型Alaya。
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<br>2,图像文本特征编码器来自[blip2-opt-2.7b](https://huggingface.co/Salesforce/blip2-opt-2.7b)的Qformer。
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<br>3,图像文本特征到大预言模型的线性投影器。
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模型的训练主要基于[LLaVA](https://github.com/haotian-liu/LLaVA)架构
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2024.01.23 最终在[MMBench](https://mmbench.opencompass.org.cn)线上测试中文测试集分数为56.9,英文测试集分数为59.8。
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推理可以参考 [inference.py](https://github.com/bingwork/MMAlaya/blob/inference/inference.py)
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