Instructions to use aniketmaurya/receipt-model-2025 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aniketmaurya/receipt-model-2025 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="aniketmaurya/receipt-model-2025")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("aniketmaurya/receipt-model-2025", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload Florence2ForConditionalGeneration
Browse files- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
adapter_config.json
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"rank_pattern": {},
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"revision": "refs/pr/20",
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"target_modules": [
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"lm_head",
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"fc2",
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"rank_pattern": {},
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"revision": "refs/pr/20",
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"target_modules": [
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"Conv2d",
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"q_proj",
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"lm_head",
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"k_proj",
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"v_proj",
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"fc2",
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"o_proj",
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"linear"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 7747264
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version https://git-lfs.github.com/spec/v1
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oid sha256:a6f723ccdbcc08e761e5032746e189f4b3f332eeb14965d5db9e7eadcc13cd5a
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size 7747264
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