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", dtype="auto") - PEFT
How to use eulogik/TinyDoc-VLM-256M with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": "<|im_start|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "errors": "replace", | |
| "extra_special_tokens": [ | |
| "<image>" | |
| ], | |
| "is_local": false, | |
| "local_files_only": false, | |
| "model_max_length": 8192, | |
| "pad_token": "<|im_end|>", | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "<|endoftext|>", | |
| "vocab_size": 49152 | |
| } | |