Simple and Effective Masked Diffusion Language Models
Paper • 2406.07524 • Published • 12
LDM-ModernBERT — Language Diffusion Model
A language diffusion model built on ModernBERT-base, pretrained on Project Gutenberg and fine-tuned on Open-Orca for instruction following.
Unlike autoregressive models that generate text left-to-right, this model generates text through iterative denoising — starting from a fully masked sequence and progressively unmasking tokens until a coherent output emerges.
Model Details
| Property | Value |
|---|---|
| Base model | ModernBERT-base |
| Parameters | ~150M |
| Architecture | Masked Language Model (diffusion objective) |
| Pretrain data | Project Gutenberg (6,400,553 train chunks, seq_len=1024) |
| SFT data | Open-Orca (~4.2M Q&A pairs) |
| Pretrain steps | 30,000 |
| SFT steps | 10,000 |
| Effective batch size | 128 |
| Pretrain LR | 5e-5 (cosine, 1500 warmup steps) |
| SFT LR | 1e-5 (cosine, 300 warmup steps) |
| Hardware | RTX 4090 24GB |
| Pretrain time | ~20 hours |
| SFT time | ~4.3 hours |
Training
Pretraining
The model is pretrained using a flow-matching diffusion objective: at each step, a random fraction t of tokens is masked, and the model learns to predict the original tokens. The loss is scaled by 1/t to account for the difficulty of predicting heavily masked sequences.
- Dataset: Project Gutenberg (6,400,553 train chunks, 34,287 test chunks)
- Initial train loss: 3.887 | Initial val loss: 3.922
- Final train loss: 2.917 | Final val loss: 2.962
SFT (Supervised Fine-Tuning)
Fine-tuned on Open-Orca instruction-response pairs. Loss is computed only on the response tokens (not the instruction), using a query mask to identify answer boundaries.
- Dataset: Open-Orca (~4.2M Q&A pairs)
- Initial train loss: 1.559 | Initial val loss: 1.333
- Final train loss: 0.837 | Final val loss: 0.967
Inference
The model supports two generation strategies:
random— masked tokens are randomly re-masked at each steplow_confidence— the lowest confidence tokens are re-masked, leading to more coherent outputs
Quickstart
from transformers import AutoModelForMaskedLM
from safetensors.torch import load_file
import torch
# Load model
model = AutoModelForMaskedLM.from_pretrained("answerdotai/ModernBERT-base")
state_dict = load_file("model.safetensors")
model.load_state_dict(state_dict, strict=False)
model.eval()
Or use the provided inference scripts:
# Interactive inference
bash inference.sh
# Generate GIF
bash create_gif.sh
Limitations
- Trained on a relatively small dataset (Project Gutenberg) with limited steps — quality is lower than production-scale models
- SFT data was truncated to 1024 tokens; very long responses may be cut off
- No RLHF or safety fine-tuning applied
Citation
Built following the approach from: