Visual Question Answering
Transformers
ONNX
Safetensors
PyTorch
PEFT
English
tinydoc_vlm
text-generation
document-understanding
ocr
vqa
vision-language-model
tinyml
siglip
lora
open-source
huggingface
multimodal
document-ai
deep-learning
form-understanding
table-extraction
receipt-ocr
invoice-processing
smollm
fine-tuning
edge-deployment
cpu-inference
low-resource
apache-2-0
small-language-model
slm
document-processing
text-recognition
structured-extraction
Instructions to use eulogik/TinyDoc-VLM-256M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eulogik/TinyDoc-VLM-256M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("visual-question-answering", model="eulogik/TinyDoc-VLM-256M")# Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("eulogik/TinyDoc-VLM-256M", device_map="auto") - PEFT
How to use eulogik/TinyDoc-VLM-256M with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08f7c5e18bd898dd547e9614f3ee494e0d04bd30be876b862e197e76f0870908
- Size of remote file:
- 30.6 kB
- SHA256:
- 5371799824632e2555ee4a560dc03623022ddb1bce3a5ad78c76e63c7f8ec590
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