Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+
+
+# eccDNAMamba  
+**A Pre-Trained Model for Ultra-Long eccDNA Sequence Analysis**
+
+---
+
+### Model Overview
+**eccDNAMamba** is a **bidirectional state-space model (SSM)** designed for efficient and topology-aware modeling of **extrachromosomal circular DNA (eccDNA)**.  
+By combining **forward and reverse Mamba-2 encoders**, **motif-level Byte Pair Encoding (BPE)**, and a lightweight **head–tail circular augmentation**, it captures wrap-around dependencies in ultra-long (10–200 kbp) genomic sequences while maintaining linear-time scalability.  
+The model provides strong performance across cancer-associated eccDNA prediction, copy-number level estimation, and real vs. pseudo-eccDNA discrimination tasks.
+
+---
+
+### Quick Start
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("eccdna/eccDNAMamba-1M")
+model = AutoModelForMaskedLM.from_pretrained("eccdna/eccDNAMamba-1M")
+
+sequence = "ATGCGTACGTTAGCGTACGT"
+inputs = tokenizer(sequence, return_tensors="pt")
+outputs = model(**inputs)
+
+# Access logits or reconstruct masked spans
+logits = outputs.logits