---
license: apache-2.0
language:
- en
datasets:
- Skylion007/openwebtext
tags:
- masked-diffusion
- diffusion-language-model
- mamba
- mamba-2
- state-space-model
- mdlm
- text-generation
- pytorch-lightning
---

# DiffMamba — Checkpoints

Training checkpoints for **DiffMamba**, a small-scale independent study of
**bidirectional Mamba-2 (state-space) denoisers for Masked Diffusion Language
Models (MDLM)**. The Transformer/DiT denoiser in MDLM is replaced with a
**bidirectional Mamba-2 backbone**, and a matched set of models is trained from
scratch on OpenWebText for a controlled quality / scaling / efficiency comparison.

> **Code, full technical report, and documentation:**
> 👉 **https://github.com/shivnarainms22/DiffMamba**
>
> This repo holds **weights only**. The GitHub repository is the source of
> truth for architecture, training recipe, evaluation, and the honest write-up
> of results and limitations.

This work builds directly on **MDLM** (Sahoo et al., NeurIPS 2024) and is a
small-scale reproduction of the research direction introduced by
**DiffuApriel / DiffuMamba** (arXiv 2511.15927). It is **not** claimed as a
novel architecture.

---

## What's in this repo

Six training runs, each in its own folder. Within a folder you'll find periodic
snapshots `step_<N>.ckpt` (every 5000 steps; every 3000 for the 50M run) and
`last.ckpt` (the final-step weights). These are **PyTorch Lightning
checkpoints** from the MDLM codebase — they bundle model weights *and* EMA
shadow parameters (EMA decay 0.9999), optimizer state, and config. They are
**not** `transformers`-loadable via `from_pretrained`; load them with the
training repo (see *How to use* below).

| Folder | Backbone | Params | LR | Steps | Tokens | Val PPL ↓ |
|--------|----------|:------:|:--:|:-----:|:------:|:---------:|
| `runB_transformer_130m`  | Transformer (DiT) | \~130M | 3e-4 | 76k | \~5B | **70.5** |
| `runD_130m_seed1`        | BiMamba-2 (SSM)   | \~130M | 3e-4 | 76k | \~5B | 85.9 |
| `runD_130m_seed2`        | BiMamba-2 (SSM)   | \~130M | 3e-4 | 76k | \~5B | 83.5 |
| `runD_130m_lr1e3_seed1`  | BiMamba-2 (SSM)   | \~130M | **1e-3** | 76k | \~5B | **79.3** |
| `scaling_100m`           | BiMamba-2 (SSM)   | \~100M | 3e-4 | 60k* | \~4B | 97.5 |
| `scaling_50m`            | BiMamba-2 (SSM)   | \~50M  | 3e-4 | 30k | \~2B | 136.3 |

Val PPL = MDLM ELBO-bound validation perplexity on the OpenWebText validation
split, measured on each run's **best EMA checkpoint** (lower is better).
`*` the 100M run's valid final checkpoint is `step_60000`/`last.ckpt`
(see the GitHub report for why 61k looped).

### Results at a glance

- **Quality.** With the MDLM (Transformer-tuned) recipe at matched 130M / \~5B
  tokens, the Transformer denoiser (70.5) is modestly but consistently stronger
  than pure BiMamba-2. BiMamba prefers a **\~3.3× higher learning rate**; a
  50M LR sweep found `1e-3` best, and retraining 130M at `1e-3` (the
  `runD_130m_lr1e3_seed1` checkpoints) closes **\~43%** of the gap (85.9 → 79.3)
  but does not close it.
- **Scaling** (BiMamba, lr 3e-4): 50M → 136.3, 100M → 97.5, 130M → 84.7 —
  clean, monotonic, seed-stable (Δ≈2.4 between seeds).
- **Efficiency.** Forward-pass latency is **textbook-linear** in sequence
  length for BiMamba vs. empirically O(L^1.55) for DiT (with FlashAttention);
  crossover at \~3K tokens, **3.12× faster at 32K**.
- **Honest finding:** *pure* BiMamba-2 trades quality for long-context
  throughput — consistent with DiffuApriel, where a *hybrid* Mamba+attention
  model is what recovers quality.

Full numbers, caveats, and the LR-fairness analysis are in the
[technical report on GitHub](https://github.com/shivnarainms22/DiffMamba/blob/master/DiffMamba_Report.md).

---

## Model details

- **Framework:** MDLM — absorbing-state discrete diffusion, SUBS
  parameterization, loglinear noise schedule, continuous time (T=0).
- **Tokenizer:** GPT-2 BPE (vocab 50257 + 1 mask token).
- **Sequence length:** 1024.
- **BiMamba-2 backbone** (`models/dimamba.py`): forward + flipped-reverse
  Mamba-2 with weight-tied projections and **AdaLN** noise-level conditioning,
  Mamba-2 defaults `d_state=64`, `headdim=64`, `cond_dim=128`, dropout 0.1.
  - 130M = hidden 768 / 12 blocks · 100M = hidden 640 / 10 blocks ·
    50M = hidden 512 / 8 blocks.
- **Transformer baseline** (`models/dit.py`): DiT, hidden 768 / 12 blocks /
  12 heads.
- **Training:** AdamW (wd 0.01, β=(0.9, 0.999), eps 1e-8), constant LR with
  warmup, gradient clip 1.0, `bf16-mixed`, global batch 64 (micro-batch 16 ×
  grad-accum 4), EMA 0.9999, single A100 per run on an academic SLURM cluster
  with 8-hour-wall checkpoint/resume job-chaining.
- **Data:** OpenWebText (`Skylion007/openwebtext`), GPT-2-tokenized,
  \~40:1 tokens-per-parameter recipe.

## How to use

These are Lightning checkpoints for the [DiffMamba / MDLM codebase](https://github.com/shivnarainms22/DiffMamba),
not `from_pretrained`-loadable. To evaluate or resume:

```bash
git clone https://github.com/shivnarainms22/DiffMamba
cd DiffMamba
# set up the environment (see requirements.yaml / scripts/)

# download a checkpoint, e.g. the LR-tuned BiMamba-130M
huggingface-cli download Shiv-22/diffmamba-checkpoints \
  runD_130m_lr1e3_seed1/last.ckpt --local-dir ./ckpts

# validation perplexity (EMA), matching the table above
python main.py mode=ppl_eval +experiment=runD_130m \
  eval.checkpoint_path=./ckpts/runD_130m_lr1e3_seed1/last.ckpt \
  data.cache_dir=<path>/data loader.eval_batch_size=32

# generate samples
python main.py mode=sample_eval +experiment=runD_130m \
  eval.checkpoint_path=./ckpts/runD_130m_lr1e3_seed1/last.ckpt \
  loader.eval_batch_size=4
```

Use the matching `+experiment=` for each folder: `runD_130m` (BiMamba-130M and
its LR-tuned variant), `runB_transformer_130m` (DiT-130M), `scaling_100m`,
`scaling_50m`.

## Limitations

Small scale (50–130M, ≤5B tokens), single-GPU academic compute, forward-pass-only
efficiency benchmark, and a Transformer-tuned training recipe that BiMamba is
shown to be undertuned for. Pure BiMamba-2 does **not** match the Transformer on
quality at this scale. Treat these as a reproduction/portfolio artifact, not a
production model. See the GitHub report for the full limitations section.

## Citation & attribution

Built on **MDLM** (Sahoo et al., *Simple and Effective Masked Diffusion Language
Models*, NeurIPS 2024; [code](https://github.com/kuleshov-group/mdlm)) and
reproduces the direction of **DiffuApriel / DiffuMamba**
(*High-Throughput Diffusion LMs with Mamba Backbone*, arXiv 2511.15927, 2025).

```bibtex
@inproceedings{sahoo2024simple,
  title={Simple and Effective Masked Diffusion Language Models},
  author={Subham Sekhar Sahoo and Marianne Arriola and Aaron Gokaslan and Edgar Mariano Marroquin and Alexander M Rush and Yair Schiff and Justin T Chiu and Volodymyr Kuleshov},
  booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
  year={2024},
  url={https://openreview.net/forum?id=L4uaAR4ArM}
}
```

License: Apache-2.0 (inherited from MDLM).