# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("nihilisticneuralnet/HinDiffusionLM")
model = AutoModelForMaskedLM.from_pretrained("nihilisticneuralnet/HinDiffusionLM")Quick Links
HinDiffusionLM: Diffusion Language Model for Hindi Language
Turning BERT-based model into an instruct-tuned LLADA-style Diffusion LLM on Hindi instruction data using a masked language modeling approach with diffusion-style generation. The model learns to iteratively denoise masked tokens to generate coherent responses in Hindi (trained on Kaggle GPU T4*2).
Experiments
Models Evaluated
| Model | Performance |
|---|---|
google/muril-base-cased |
Best |
google/muril-large-cased |
Poor |
ai4bharat/indic-bert |
Moderate |
Datasets Tested
| Dataset | Subset | Status | Notes |
|---|---|---|---|
ai4bharat/indic-instruct-data-v0.1 |
anudesh |
Used | Primary dataset for demonstration |
ai4bharat/indic-instruct-data-v0.1 |
lm_sys |
Skipped | Too time-intensive for training & GPU constraints |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nihilisticneuralnet/HinDiffusionLM")