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NYCU-MLLab/Diffusion-Learning-via-Strategic-Denoising-and-Remasking
Diffusion-Learning-via-Strategic-Denoising-and-Remasking is a text generation model from NYCU-MLLab. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
[](https://github.com/leotsia0416/DRPO) [](https://github.com/NYCU-MLLab/Diffusion-Learning-via-Strategic-Denoising-and-Remasking)
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Updated Aug 25, 2026
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From the Hugging Face model README
This repository provides the final tensor checkpoint for DRPO 1.7B, trained with strategic denoising and remasking. It is intended to be used with the canonical training and Hugging Face evaluation code in the DRPO reproduction repository.
| Item | Value |
|---|---|
| Base model | JetLM/SDAR-1.7B-Chat |
| Parameter scale | 1.7B |
| Training checkpoint | checkpoint-450 |
| Evaluation benchmark | MATH-500 |
| Reported result | 322/500 (64.4%) |
| Weight format | sharded safetensors |
The reproduction repository provides a one-command environment setup, pinned model/data revisions, the canonical model implementation, and the exact MATH-500 recipe.
git clone https://github.com/leotsia0416/DRPO.git
cd DRPO
python3 setup.py
sbatch script/test_gap.sh math500
The evaluation parameters are recorded in script/config/eval_math500.yaml. setup.py automatically downloads this checkpoint and the required base-model assets.
Only the final model tensors and safetensors index are published here:
model-00001-of-00002.safetensorsmodel-00002-of-00002.safetensorsmodel.safetensors.index.jsonExecutable model code, tokenizer assets, optimizer/scheduler states, RNG states, and checkpoint-local source snapshots are intentionally excluded. This prevents stale checkpoint code from overriding the canonical implementation in Git.
The archived evaluation predictions score 322/500 (64.4%), and the reproduction repository pins the same MATH answer-extraction rule. Generated text can still vary across GPU jobs because BF16 kernels and FlashAttention are not guaranteed to be bit-identical across nodes, drivers, and CUDA stacks, even with temperature: 0.0.
caa92001f2b4b62433db1b8b16702ba35726b07b1442cf814bdfd4b1951fee0f model-00001-of-00002.safetensors
e5a3575ce8b1481d7ba949c841c37edc7c13f70262b69b29164c85d37a33441e model-00002-of-00002.safetensors
cb0bbd72bc89e405e31718fe07d9d1fb560faf4795d64811e572fa7216d085bb model.safetensors.index.json