Add pipeline tag, library name, and paper link to model card

README.md

@@ -1,7 +1,15 @@
 ---
+datasets:
+- turkish-nlp-suite/Havadis
+- turkish-nlp-suite/temiz-OSCAR
+- wikimedia/wikipedia
 language:
 - tr
 license: apache-2.0
+metrics:
+- perplexity
+library_name: transformers
+pipeline_tag: text-generation
 tags:
 - turkish
 - diffusion
@@ -9,30 +17,24 @@ tags:
 - 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. 
+This model is presented in the paper [Diffutron: A Masked Diffusion Language Model for Turkish Language](https://huggingface.co/papers/2603.20466). It represents the completion of the **Continual Pre-training (CPT)** phase, where it 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)
+* **Base Architecture:** `jhu-clsp/mmBERT-base` (ModernBERT-based architecture)
 * **Language:** Turkish
 * **Parameter Count:** 307M (0.3B)
 * **Context Length:** 512 tokens
-* **Training Libraries:** `dllm`, PyTorch
+* **Training Libraries:** `dllm`, PyTorch, `transformers`
 * **Status:** Foundation / Base Model (Post-CPT)
 
 ## 🚀 Architecture & Continual Pre-training (CPT)