culturalheritagenus/Rumi-degarbler-Gemma-v1

Model Card for Model ID

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Model Details

Model Description

This model is trained to produce clean Jawi text from a garbled source, as part of an OCR pipeline.

• —Developed by: Computational Cultural Heritage Research Group (NUS).
• —Model type: Gemma 2 9B
• —Language(s) (NLP): Malay, English
• —Finetuned from model aisingapore/Gemma-SEA-LION-v3-9B-IT

How to Get Started with the Model

Use the code below to get started with the model:

from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

trained_model = AutoModelForCausalLM.from_pretrained(
    "culturalheritagenus/rumi-degarbler-v1",
    device_map="auto",
    torch_dtype=torch.bfloat16
)
trained_tokenizer = AutoTokenizer.from_pretrained("culturalheritagenus/rumi-degarbler-v1")

To perform inference:

messages = [
    {"role": "user", "content": "You are a Malay language spelling corrector. I will give you some text written in messy Rumi (shortened or mistyped). Rewrite it in correct Malay Rumi spelling.\naurng ank. yngdim dimn anm aurngdan"},
]
inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True, # Must add for generation
    return_tensors = "pt",
).to("cuda")


text_streamer = TextStreamer(tokenizer)
_ = trained_model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128, use_cache = True)

Training Details

Training Data

The model was trained on culturalheritagenus/rumi-correction-v2-data-v6-real

Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• —Hardware Type: 1x GH200 (96 GB)
• —Hours used: < 72
• —Cloud Provider: Lambda
• —Compute Region: US-East (Lambda Labs)

Technical Specifications

Software

• —Python version: 3.10.12
• —CUDA version: 12.8
• —Torch version: 2.7.1+cu128