diffutron
/

DiffutronLM-0.3B-Base

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 ---
+language:
+- tr
+license: apache-2.0
+tags:
+- turkish
+- diffusion
+- masked-diffusion
+- non-autoregressive
+- foundation-model
+- dllm
+datasets:
+- turkish-nlp-suite/Havadis
+- turkish-nlp-suite/temiz-OSCAR
+- wikimedia/wikipedia
+metrics:
+- perplexity
 ---
+# DiffutronLM-0.3B-Base
+**DiffutronLM-0.3B-Base** is the foundational Masked Diffusion Language Model (MDLM) of the Diffutron series, tailored specifically for the Turkish language.
+This model represents the completion of the **Continual Pre-training (CPT)** phase. It has successfully adapted the multilingual representations of its backbone to the agglutinative complexity and morphological nuances of Turkish.
+⚠️ **Note:** This is a base foundation model. It has **not** been instruction-tuned or aligned for chat capabilities. If you are looking for a model that follows prompts and answers questions, please use `DiffutronLM-0.3B-Instruct`.
+## 📌 Model Details
+* **Model Type:** Masked Diffusion Language Model (MDLM) Base
+* **Base Architecture:** `jhu-clsp/mmBERT-base` (Multilingual Encoder)
+* **Language:** Turkish
+* **Parameter Count:** 307M (0.3B)
+* **Context Length:** 512 tokens
+* **Training Libraries:** `dllm`, PyTorch
+* **Status:** Foundation / Base Model (Post-CPT)
+## 🚀 Architecture & Continual Pre-training (CPT)
+Unlike standard autoregressive models, Diffutron models text generation as a discrete diffusion process. To align the base encoder's latent space with the Turkish target distribution while preserving cross-lingual reasoning, this model underwent a specialized CPT pipeline:
+* **Data Curation:** Trained on a composite dataset of approximately 2 million sequences (max length 512) sourced from:
+  * **Havadis:** Comprehensive Turkish news articles.
+  * **Temiz-OSCAR:** A cleaned, filtered subset of the Common Crawl-based Turkish OSCAR corpus.
+  * **Turkish Wikipedia:** High-quality encyclopedic sequences.
+* **Efficient Adaptation via LoRA:** Instead of full-parameter fine-tuning which risks catastrophic forgetting, we applied Low-Rank Adaptation (LoRA) with a high rank ($r=256$, $\alpha=256$) targeting all linear modules (Attention Q, K, V, O and MLP Input, Output). This resulted in ~14.94% trainable parameters.
+* **Objective:** Masked Language Modeling (MLM).
+## 📊 Intrinsic Evaluation
+To quantify the improvements gained from the CPT phase, we conducted an intrinsic evaluation using perplexity on the **Bilkent Turkish Writings Dataset** (evaluated with a masked language modeling probability of 0.15).
+The CPT process resulted in a significant reduction in perplexity, indicating a strong alignment with Turkish linguistic structures:
+* **jhu-clsp/mmBERT-base (Pre-CPT):** 3.42
+* **DiffutronLM-0.3B-Base (Post-CPT):** **2.75**
+*(Note: Downstream task evaluations on the CETVEL benchmark were conducted on the Instruct-tuned versions of this model.)*
+## 💻 Usage
+As a base masked diffusion model, this checkpoint is ideal for:
+1. **Further Fine-tuning:** Acting as a starting point for domain-specific continued pre-training or custom instruction tuning.
+2. **Masked Token Prediction:** Filling in blanks or reconstructing corrupted text.
+3. **Unconditional/Conditional Generation:** Generating text using a discrete diffusion sampling loop (e.g., via the `dllm` library).
+Because it uses a non-autoregressive paradigm, standard `AutoModelForCausalLM.generate()` pipelines will not work. Please utilize discrete diffusion generation strategies.
+## ⚠️ Limitations
+* **No Instruction Tuning:** Will not respond to QA prompts or instructions naturally.
+* **Multilingual Backbone:** While heavily adapted to Turkish, it is built upon a multilingual encoder.
+* **Context Window:** Restricted to a 512-token context window during the base phase.
+## 📝 Citation
+```bibtex
+@misc{diffutron2026,
+  author = {Kocabay, Şuayp Talha and Akkuş, Talha Rüzgar},
+  title = {Diffutron: A Masked Diffusion Language Model for Turkish Language},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{[https://huggingface.co/collections/diffutron/diffutronlm](https://huggingface.co/collections/diffutron/diffutronlm)}}
+}