I recently stumbled upon a fascinating paper on language diffusion models by Nie et al. (2025), and I just had to try replicating part of it. With the help of Hugging Face’s Transformers, I was amazed to find that I could implement the training script in under 80 lines of code!
Using DistilBERT, I fine-tuned the model on the TinyStories dataset and was thrilled to see the results exceed my expectations.
## The Power of Language Diffusion
Language diffusion models have the potential to revolutionize the way we generate text. By learning to predict the next word in a sequence, these models can create coherent and natural-sounding language.
## A Simple yet Effective Approach
What struck me about this approach was its simplicity. By leveraging pre-trained models and existing datasets, I was able to achieve impressive results with minimal code.
## The Project
If you’re interested in exploring language diffusion further, I’ve made the project available on GitHub at
## Example in Action
Take a look at this example of generating tiny stories via a reverse language diffusion process:
## The Future of Language Generation
Language diffusion models have the potential to open up new possibilities in text generation. I’m excited to see where this technology takes us and how we can apply it to real-world problems.
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*Further reading: [Large Language Diffusion Models by Nie et al. (2025)](https://arxiv.org/abs/2502.09992)*