nanoDiff

A minimal, clean, hackable implementation of a state-of-the-art diffusion language model, built to learn, understand, train, and improve dLLMs.

Think of it as nanoGPT / nanochat, but for diffusion language models instead of autoregressive ones. It distills the LLaDA recipe (the simplest formulation that has scaled to 8B–100B) down to a small modular package you can read in an afternoon.

Includes pretraining (pretrain/train.py, FineWeb-Edu by default) and instruction fine-tuning (sft/train.py, Alpaca-cleaned). Evaluation: held-out perplexity via eval.py and LAMBADA accuracy / PPL via benchmark/lambada.py.

Based on LLaDA: Large Language Diffusion Models (Nie et al. 2025, arXiv:2502.09992). The broader lineage (D3PM → LLaDA 2.0) is in References.

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How it works

A masked diffusion LM denoises text instead of writing it left-to-right.

• Forward process (diffusion.py): sample t ~ U(0,1), replace each token with [MASK] independently with probability t. No Gaussians, no latents.
• Model (model.py): LLaMA-style transformer (RMSNorm, SwiGLU, RoPE) with bidirectional attention. The only architectural change from a GPT.
• Loss (diffusion.py): 1/t-weighted cross-entropy on masked positions only. The weight makes it a real NLL upper bound, not a heuristic.
• Sampling (sampler.py): start fully masked, predict every position, commit the most confident, re-mask the rest, iterate. See docs/sampling.md for the full algorithm.

The training step is five lines, from pretrain/train.py:

x0=train_data.get_batch(...) # clean tokensx_t, mask, t=forward_process(x0, mask_token_id) # corrupt themlogits=model(x_t) # predict every tokenloss=diffusion_loss(logits, x0, mask, t) # 1/t-weighted CE on masksloss.backward()

No noise schedule, no timestep embedding (LLaDA's time-free parameterization), no ELBO bookkeeping.

 t=1 ████████████████████ (all [MASK])
██ the ███ of ████ is
██ the meaning of life is
t=0 the meaning of life is (clean text)
▲ each step: predict, commit confident, repeat

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Quickstart

uv sync # create .venv, install deps + the nanodiff packagesource .venv/bin/activate # (or prefix the commands below with `uv run`)
python tests/smoke_test.py # optional: verify the core stack works (~2 min, CPU)# 1. tokenize a pretraining corpus (downloads FineWeb-Edu, then tokenizes)
python scripts/prepare_data.py --out-dir data/fineweb_edu --num-tokens 2_000_000_000
# 2. pretrain a base model (single GPU)
python pretrain/train.py --config pretrain/configs/50m.py
# ...or multi-GPU:
torchrun --standalone --nproc_per_node=8 pretrain/train.py --config pretrain/configs/50m.py
# 3. chat / evaluate
python chat.py --ckpt checkpoints/50m/ckpt.pt
python eval.py --ckpt checkpoints/50m/ckpt.pt --iters 500
# 4. (optional) instruction-tune the base on Alpaca-cleaned
python scripts/prepare_sft_data.py --out-dir data/alpaca_sft
python sft/train.py --config sft/configs/50m_alpaca.py

Scaling

Scaling is a one-file change: copy a config and edit the model/optimizer fields.

# pretrain/configs/350m.pyfromnanodiff.configimportConfigconfig=Config(name="nanodiff-350m", n_layer=16, n_embd=1280, n_head=20,
batch_size=16, grad_accum_steps=16, out_dir="checkpoints/350m")

Everything reads from the Config dataclass, so model code never changes.

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Pretrained models

Six pretrained checkpoints are on the Hugging Face Hub:

Click to expand: model list, download commands, speed knobs, scaling, benchmarks

Model	What it is
Sebasdi/nanodiff-50m-base	the 50M base, pretrained on ~2B tokens of FineWeb-Edu (val perplexity ~50)
Sebasdi/nanodiff-150m-base	the 150M base, pretrained on ~3B tokens of FineWeb-Edu (val perplexity ~44)
Sebasdi/nanodiff-350m-base	the 350M base, pretrained on ~10B tokens of FineWeb-Edu (val perplexity ~29, LAMBADA 36.95%)
Sebasdi/nanodiff-50m-sft-alpaca	the 50M base, instruction-tuned on Alpaca-cleaned (~51k examples)
Sebasdi/nanodiff-150m-sft-alpaca	the 150M base, instruction-tuned on Alpaca-cleaned: meaningfully better than the 50M SFT (LAMBADA 15.74% vs 14.32%)
Sebasdi/nanodiff-350m-sft-alpaca	the 350M base, instruction-tuned on Alpaca-cleaned (best SFT val ~1.07, LAMBADA 25.13%)

Pick any model name from the table above; the pattern is the same:

# Base model (continues text, prompt document-style):
hf download Sebasdi/nanodiff-350m-base nanodiff-350m-base.pt --local-dir checkpoints/
python chat.py --ckpt checkpoints/nanodiff-350m-base.pt
# SFT model (follows instructions, note the --sft flag):
hf download Sebasdi/nanodiff-350m-sft-alpaca nanodiff-350m-sft-alpaca.pt --local-dir checkpoints/
python chat.py --ckpt checkpoints/nanodiff-350m-sft-alpaca.pt --sft

Sampling speed

chat.py defaults to the validated fast preset (--compile --steps 32 --block-length 32). On the 150M SFT (DGX Spark / GB10, sampling at temp=0.8 top-p=0.9 rep-penalty=3 gen-length=96):

Configuration	tok/s	Speedup
pre-optimization baseline	236	1.00×
chat.py default (--compile --steps 32)	965	4.09×
--block-length 16 (faster, but short answers on casual prompts)	1038	4.40×
--no-compile --steps 96 (max quality, no warmup)	236	1.00×
--no-compile --use-cache --tau 0.5 (compile-free fast)	355	1.51×

The flags:

• --compile / --no-compile: torch.compile(model) for kernel fusion. Default ON. One-time ~5–30 s warmup on the first generation; subsequent generations are ~1.4× faster. Pass --no-compile to skip the warmup for one-off queries or on builds without Triton.
• --steps: denoising iterations. Default 32 (~3 commits per step at gen-length=96). The sampler applies a within-step repetition penalty so multiple positions committed in the same step don't collide on the same token ("process process"). Bump to --steps 96 for the LLaDA one-commit-per-step quality maximum (~3× slower). Below --steps 24 some across-step doubling reappears. See docs/sampling.md for the full algorithm walkthrough.
• --use-cache: Fast-dLLM block-wise K/V prefix cache (Lou et al., 2025). Approximate but measured LAMBADA-equivalent (15.74% → 15.72%, 1/5153). Pays off at --gen-length≥256 or batched generation; skip when --compile is on (they target the same overhead and don't stack).
• --tau 0.5: confidence-threshold parallel decoding. Commits every position with model-confidence ≥ τ in this step instead of the fixed schedule. Best with --use-cache (i.e., on the compile-free path).

Scaling

A small, controlled scaling result so far. All numbers are from eval.py (500 batches on a held-out FineWeb-Edu split):

Model	Tokens	Val NLL	Perplexity
50M	2B	3.92	50.6
50M (matched-token control)	3B	3.91	50.1
150M	3B	3.78	43.8
350M	10B	3.38	29.3

At matched 3B tokens (rows 2-3), same block_size, same schedule, same data shard, only the model and its appropriately-scaled LR differ, the 150M wins by 0.13 nats (~13% perplexity). The control row is what makes that defensible: it shows the 50M, given the same 3B-token budget, only moves its loss by ~0.01 nats versus its 2B baseline. The 50M is capacity-floored at ~3.91; the 150M lands below that floor. So the gap is capacity, cleanly isolated, not "trained longer" and not "saw more tokens".

The 150M→350M step (10B tokens, Chinchilla-optimal at ~7B + headroom) delivers the largest jump in the family: -0.38 nats, ~31% perplexity reduction. The training was still improving when it ended; the val curve hadn't flattened, suggesting the 350M is not at its capacity ceiling at 10B tokens. Pushing to ~15B tokens would likely give another 0.05-0.10 nats.

Benchmarks

LAMBADA last-word prediction on the public test split (5153 examples; single-pass diffusion scoring, see benchmark/lambada.py):

Model	LAMBADA acc	LAMBADA PPL
50M base	19.83%	834
50M SFT	14.32%	3344
150M base	21.89%	358
150M SFT	15.74%	1606
350M base	36.95%	55.3
350M SFT	25.13%	286.6

Capacity helps both stages. Base LAMBADA climbs 19.83 → 21.89 → 36.95% across 50M / 150M / 350M; SFT mirrors (14.32 → 15.74 → 25.13%).

The 150M → 350M jump outpaces val PPL. Val PPL improved 33%, but LAMBADA PPL improved 84% and accuracy jumped +15 pp (+69% relative). The bigger model is spending capacity on long-range and discourse representations, not just sharper local statistics. A capability signal, not just a fitting one.

The alignment tax grows with scale. At 50M and 150M, base → SFT costs ~28% of base accuracy, which looked like a capacity-independent distribution shift. At 350M the tax jumps to 32% (an 11.8 pp drop vs ~6 pp at smaller scales), and the SFT/base PPL blowup ratio grows in parallel (4.0× → 4.5× → 5.2×). More world-modeling capability means more to lose when SFT shifts the distribution toward Alpaca instruction-format.

Calibration vs GPT-2 (124M ~32%, 355M ~46% on LAMBADA): per-parameter is the least informative framing. GPT-2 saw ~40B tokens (~4× more than our 350M's 10B), on a distribution closer to LAMBADA's BookCorpus origin, scored autoregressively with teacher-forcing across multi-token targets; our single-pass diffusion scoring conditions each masked position on the prefix alone.

(MMLU, HellaSwag, ARC are still at random chance at 350M scale, so they're not run yet. See benchmark/README.md for the rationale.)

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Training customization

Click to expand: dataset / tokenizer / model size swap notes

Dataset, fully swappable. The pipeline only ever sees a flat uint16 token array on disk, so it is dataset-agnostic. Either point prepare_data.py at any Hugging Face text dataset (it just needs a "text" field):

python scripts/prepare_data.py --dataset <hf-name> --subset <config> --out-dir data/mine

or produce your own train.bin / val.bin (any uint16 token dump) and set data_dir in your config; the model never knows the difference.

Tokenizer, coupled but in known places. The GPT-2 BPE is wired in as the default working path. Swapping it means updating these spots:

File(s)	What to change
scripts/prepare_data.py, chat.py, pretrain/train.py	tiktoken.get_encoding("gpt2")
nanodiff/config.py	vocab_size, mask_token_id (= last real id + 1, then pad)
scripts/prepare_data.py, chat.py, pretrain/train.py	EOT, the document-separator id
nanodiff/data.py	uint16 dtype caps the vocab at 65536; use uint32 above that

Model size, a one-file config change. See Scaling above.

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References

The recipe nanoDiff implements is LLaDA; here is the lineage:

Paper	Year	arXiv
D3PM: Structured Denoising Diffusion in Discrete State-Spaces	2021	2107.03006
SEDD: Discrete Diffusion by Estimating Data-Distribution Ratios	2024	2310.16834
MDLM: Simple and Effective Masked Diffusion Language Models	2024	2406.07524
BD3-LM: Block Diffusion (interpolating AR ↔ diffusion)	2025	2503.09573
LLaDA: Large Language Diffusion Models (primary reference)	2025	2502.09992
Dream 7B: Diffusion Large Language Models	2025	2508.15487
LLaDA 2.0: Scaling Diffusion Language Models to 100B	2025	2512.15745
A Survey on Diffusion Language Models	2025	2508.10875