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
retired-checkpoint
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 # Warning: Pipeline type "visual-question-answering" 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("visual-question-answering", model="eulogik/TinyDoc-VLM-256M")# pip install -U transformers accelerate # 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
Download processor_config.json from eulogik/TinyDoc-VLM-256M: direct link, hf CLI and curl.
- Browser
- Download file 102 Bytes
-
https://huggingface.co/eulogik/TinyDoc-VLM-256M/resolve/main/processor_config.json
- Command line
-
hf download hf://eulogik/TinyDoc-VLM-256M/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/eulogik/TinyDoc-VLM-256M/resolve/main/processor_config.json
102 Bytes
| { | |
| "processor_class": "TinyDocVLMProcessor", | |
| "image_token": "<image>", | |
| "image_token_id": 49153 | |
| } | |