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LoNebula
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Model Details
Model Provider
LoNebula
Model Tree
HuggingFaceTB/SmolVLM-500M-Instruct
Input Modalities
Output Modalities
Supported Functionality
bash
pip install torch transformers pillowpython
from PIL import Imageimport torchfrom transformers import AutoModelForImageTextToText, AutoProcessor repo = "LoNebula/okinawa-sign-vlm"processor = AutoProcessor.from_pretrained(repo)model = AutoModelForImageTextToText.from_pretrained(repo)image = Image.open("sign.png").convert("RGB") messages = [{ "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": ( "Read the Japanese vocabulary shown in this Okinawa-style learning sign. " "Give its Shuri Okinawan dictionary form. Reply with only the dictionary form." )}, ],}]prompt = processor.apply_chat_template(messages, add_generation_prompt=True)inputs = processor(text=prompt, images=[image], return_tensors="pt")device = next(model.parameters()).deviceinputs = {key: value.to(device) if hasattr(value, "to") else value for key, value in inputs.items()}with torch.inference_mode(): generated = model.generate(**inputs, max_new_tokens=48, do_sample=False)prompt_length = inputs["input_ids"].shape[1]print(processor.batch_decode(generated[:, prompt_length:], skip_special_tokens=True)[0])The image files are original graphics generated locally by this project. The Japanese prompts and Shuri dictionary forms come from LoNebula/okinawa-ja-shuri-dictionary-translation, a Shuri-only, attributed derivative of the National Institute for Japanese Language and Linguistics' Okinawa-go Jiten index. The source dataset is marked CC BY 4.0. Each training example preserves its source ID, citation, source URL, and license. See the dataset card for the full attribution and generation recipe.
LoNebula/okinawa-sign-vlm-dataset; 600 generated image-text examples with a fixed 90/10 train/test split.National Institute for Japanese Language and Linguistics, Okinawa-go Jiten (1963; index), as packaged by doraking/ryukyuan-okinawan-corpus, CC BY 4.0. Source: https://mmsrv.ninjal.ac.jp/okinawago/. Images were generated by the dataset preparation script in this project.
from PIL import Imageimport torchfrom transformers import AutoModelForImageTextToText, AutoProcessor repo = "LoNebula/okinawa-sign-vlm"processor = AutoProcessor.from_pretrained(repo)model = AutoModelForImageTextToText.from_pretrained(repo)image = Image.open("sign.png").convert("RGB") messages = [{ "role": "user", "content": [ {"type": "image"}, {"type": "text", "text": ( "Read the Japanese vocabulary shown in this Okinawa-style learning sign. " "Give its Shuri Okinawan dictionary form. Reply with only the dictionary form." )}, ],}]prompt = processor.apply_chat_template(messages, add_generation_prompt=True)inputs = processor(text=prompt, images=[image], return_tensors="pt")device = next(model.parameters()).deviceinputs = {key: value.to(device) if hasattr(value, "to") else value for key, value in inputs.items()}with torch.inference_mode(): generated = model.generate(**inputs, max_new_tokens=48, do_sample=False)prompt_length = inputs["input_ids"].shape[1]print(processor.batch_decode(generated[:, prompt_length:], skip_special_tokens=True)[0])