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gemma-4-e4b-doc-classifier-lora
asim-salim/gemma-4-e4b-doc-classifier-lora
gemma-4-e4b-doc-classifier-lora is a image-text-to-text model from asim-salim. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as gemma.
LoRA adapter finetuned from google/gemma-4-E4B-it for single-page document classification and identity-field extraction from scanned/faxed financial documents.
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Repo size
179 MB
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.safetensors147 MB · 82%
At a glance
- Task
- Image-Text-to-Text
- Library
- peft
- License
- gemma
- Access
- Public
- Created
- Jul 27, 2026
- Updated
- Jul 27, 2026
- SHA
- a89ca272
Try a prompt
Base models
Model card
From the Hugging Face model README
Gemma 4 E4B — Document Classifier (LoRA)
LoRA adapter finetuned from google/gemma-4-E4B-it for single-page document
classification and identity-field extraction from scanned/faxed financial documents.
Classes
bank_statement, drivers_license, monthly_bill, paystub, state_id, unknown
Output format
{"schema_object_wrapper": [{"cls": "", "confidence": 0.0, "issuer": "", "holder": "", "page_marker": ""}]}
Usage
from unsloth import FastVisionModel
model, processor = FastVisionModel.from_pretrained("asim-salim/gemma-4-e4b-doc-classifier-lora")
FastVisionModel.for_inference(model)
Adapter: 147 MB LoRA over google/gemma-4-E4B-it. Trained with unsloth + TRL (PEFT 0.19.1).
License
Governed by the Gemma Terms of Use and the Gemma Prohibited Use Policy.
Limitations
Trained on a specific document corpus; not evaluated for general-purpose document understanding. Outputs must not be treated as authoritative for financial decisions.
- Task
- Image-Text-to-Text
- Library
- peft
- License
- gemma
- Created
- Jul 27, 2026
- Updated
- Jul 27, 2026