Diffutron is a parameter-efficient, Masked Diffusion Language Model (MDLM) specifically designed for the Turkish language. Unlike standard autoregressive models that generate text one token at a time, Diffutron generates text by iteratively refining sequences in parallel, allowing for simultaneous consideration of the entire sentence context.

Despite its compact size of 307 million parameters, DiffutronLM-0.3B-Instruct achieves highly competitive performance against much larger, multi-billion-parameter autoregressive baselines on Turkish NLP benchmarks.

📌 Model Details

Model Type: Masked Diffusion Language Model (MDLM)
Base Architecture: jhu-clsp/mmBERT-base (Multilingual Encoder)
Language: Turkish
Parameter Count: 307M (0.3B)
Context Length: 256 tokens (Instruct), 512 tokens (Base)
Training Libraries: dllm, PyTorch

🚀 Architecture & Approach

Diffutron departs from traditional next-token prediction. It treats text generation as a discrete diffusion process:

Forward Process: Clean text is gradually corrupted into a sequence of <mask> tokens.
Reverse Process: The model learns to denoise the sequence globally, attending to visible context bi-directionally to predict the original tokens.

This non-autoregressive paradigm compresses linguistic knowledge efficiently, allowing this 0.3B model to punch significantly above its weight class.

📚 Training Pipeline

The model was developed through a resource-efficient, multi-stage training pipeline:

1. Continual Pre-training (CPT)

To adapt the multilingual backbone to Turkish without catastrophic forgetting, we employed a high-rank LoRA strategy (r=256, α=256) targeting all linear modules (Attention and MLP).

Data: ~2 million sequences sourced from Havadis (news), Temiz-OSCAR (web), and Turkish Wikipedia.
Result: Perplexity on the Bilkent Turkish Writings Dataset dropped significantly from 3.42 (base) to 2.75.

2. Progressive Instruction-Tuning (SFT)

To unlock generative instruction-following capabilities, we utilized a two-stage supervised fine-tuning approach:

Stage 1 (General Adaptation): Trained on metunlp/LlamaTurk-Instruction-Set for 20 epochs to establish fundamental instruction-following behaviors.
Stage 2 (Complex Specialization): Trained on the nuanced turkish-nlp-suite/InstrucTurca dataset for 8 epochs with an increased batch size, enhancing the model's ability to handle intricate, domain-specific Turkish commands.

📊 Evaluation Results

The model was evaluated on a representative subset of the CETVEL Benchmark Suite. DiffutronLM-0.3B (2nd Stage) demonstrates remarkable parameter efficiency, outperforming models up to 7x its size (e.g., Kumru-2B and TURNA-1.1B) on average scores.

Benchmark	Diffutron-1st-Stage (0.3B)	Diffutron-2nd-Stage (0.3B)	TURNA (1.1B)	Kumru (2B)	Kanarya (2B)	Llama-3.2 (3B)	Trendyol (7B)	Aya-101 (13B)
Belebele_TR	22.22	27.00	22.56	29.00	28.11	55.78	36.22	22.89
EXAMS_TR	25.95	27.74	23.66	30.03	30.03	26.21	28.50	22.90
IronyTR	50.67	52.00	48.33	51.00	50.00	50.17	50.00	52.17
News_Cat	23.20	32.40	32.80	26.40	66.80	64.00	81.20	20.00
MNLI_TR	33.29	32.81	34.94	36.42	33.40	34.76	35.19	27.90
STS_TR	17.77	18.78	14.21	11.75	12.91	12.91	15.52	16.97
XCOPA_TR	53.80	52.00	55.80	54.00	64.20	54.60	61.00	59.60
Average	32.41	34.68	33.19	34.09	40.78	42.63	43.95	31.78

💻 Usage

Because Diffutron is a Masked Diffusion Language Model, it requires inference strategies distinct from standard causal generation. We recommend using the dllm library or custom generation loops tailored for discrete diffusion.

1. Install the dllm Library:

bash

git clone https://github.com/Diffutron/dllm.git
cd dllm
pip install -e .

2. Chat via Interaction Mode:

bash

python -u examples/bert/chat.py \
    --model_name_or_path "diffutron/DiffutronLM-0.3B-Instruct" \
    --chat True \
    --steps 64 \
    --max_new_tokens 64 \
    --temperature 0.1 \
    --block_length 32 \
    --repetition_penalty 1.2 \
    --remasking "low_confidence" \
    --stochastic_transfer False \
    --cfg_scale 0.0

For other inference modes, see dllm library.

Generation Parameters Used in Paper:

Longer Context: Steps: 128, Temp: 0.1, Block Length: 32, Repetition Penalty: 1.2
Shorter Context: Steps: 64, Remask: low_conf, Stochastic: False, CFG: 0.0

⚠️ Limitations

Multilingual Backbone: Built upon a multilingual encoder rather than a native Turkish foundation model.
Context Window: Restricted to a 256-token context window for generation, limiting its use in long-form summarization or document-level generation.
Data Nuances: Instruction datasets rely heavily on translations or synthetic data, which may occasionally miss subtle cultural contexts.

📝 Citation

If you use Diffutron in your research, please cite our preprint:

bibtex

@misc{diffutron2026,
      title={Diffutron: A Masked Diffusion Language Model for Turkish Language}, 
      author={Şuayp Talha Kocabay and Talha Rüzgar Akkuş},
      year={2026},
      eprint={2603.20466},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2603.20466}, 
}