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
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README.md
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@@ -16,3 +16,14 @@ OpenCompass 评测榜单,均分41.1,排名25名。
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<br>MMBench 评测榜单,开源开放的模型,中文测试集,均分58.6,排名25名。
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推理可以参考 [inference.py](https://github.com/DataCanvasIO/MMAlaya/blob/main/inference.py)
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<br>MMBench 评测榜单,开源开放的模型,中文测试集,均分58.6,排名25名。
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推理可以参考 [inference.py](https://github.com/DataCanvasIO/MMAlaya/blob/main/inference.py)
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# Citation
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MMAlaya使用<a href="https://github.com/DataCanvasIO/Alaya/blob/main/LICENSE">Apache 2.0 Lisense</a>,开放模型权重,允许商业用途。如果您的项目引用了我们的MMAlaya,请标明出处:
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```
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@misc{datacanvas2024mmalaya,
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author = {DataCanvas Ltd.},
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title = {mmalaya},
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year = {2024},
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howpublished = {\url{https://github.com/DataCanvasIO/MMAlaya}},
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}
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```
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