Update README.md

README.md

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 ---
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+- biology
+- genomics
+- dna-compression
+- causal-language-modeling
+- gpt2
+license: apache-2.0
+datasets:
+- dnabert-2
+library_name: transformers
+pipeline_tag: text-generation
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+# DNAGPT2: Genomic Large Language Model for Compression and Analysis
+
+**DNAGPT2** is a family of autoregressive (decoder-only) transformer models trained on genomic DNA sequences. 
+
+The models follow the GPT-2 architecture and are trained from scratch on a multi-species genome dataset. 
+
+## Model Details
+
+- **Model Type:** Causal Language Model (Decoder-only Transformer)
+- **Architecture:** GPT-2 Small
+  - **Parameters:** ~86 Million
+  - **Layers:** 12
+  - **Heads:** 12
+  - **Embedding Dimensions:** 768
+  - **Context Window:** 1,024 tokens
+- **Vocabulary Sizes:** Models are available with vocabulary sizes of 16, **32**, 64, 128, 256, 512, 1024, 2048, 4096, and 8192.
+
+## Intended Use
+
+These models are designed for:
+1.  **DNA Compression:** Used in conjunction with Arithmetic Encoding (AE) to compress genomic sequences.
+3.  **Sequence Modeling:** Next-token prediction for DNA sequences.
+
+**Input:** Raw DNA sequences containing the characters `A`, `C`, `G`, `T`.
+**Output:** Logits/Probabilities for the next token in the sequence.
+
+## Training Data
+
+The models were pretrained on the dataset provided by the authors of **DNABERT-2**.
+- **Composition:** 135 genomes covering Vertebrata, Fungi, Protozoa, Invertebrata, and Bacteria.
+- **Size:** Approximately 32.5 billion nucleotides.
+- **Preprocessing:** The alphabet was restricted to **A, C, G, T**. The letter **N** (unknown/ambiguous nucleotide) was omitted from the training data.
+
+## Training Procedure
+
+The models were trained using the PyTorch framework and the `nanoGPT` recipe.
+
+- **Tokenizer:** Byte-Pair Encoding (BPE) trained via SentencePiece.
+- **Epochs:** 1
+- **Optimization:** AdamW (Betas: 0.9, 0.95; Weight decay: 0.1)
+- **Learning Rate:** Cosine decay (Max: 8e-4, Min: 8e-5) with linear warmup.
+- **Batch Size:** $2^{19}$ tokens per step.
+- **Hardware:** Single NVIDIA A40 GPU.
+
+## Performance
+
+The models were evaluated on their ability to compress DNA sequences (measured in **bits per symbol** or **bps**) using Arithmetic Encoding. Lower is better.
+
+| Dataset | Metric | DNAGPT2_32 | Benchmark (gzip -9) | Benchmark (Jarvis3) |
+| :--- | :--- | :--- | :--- | :--- |
+| **Homo sapiens** (T2T-CHM13v2.0) | bits/symbol | **1.470** | 2.022 | 1.384 |
+| **M. llanfair...** (Bacteria) | bits/symbol | **1.783** | 2.142 | 1.713 |
+| **A. thaliana** (Plant - Chr1) | bits/symbol | **1.876** | 2.161 | 1.702 |
+
+The `DNAGPT2_32` model outperforms general-purpose compressors (gzip) and competitive deep learning models like `hyenaDNA` and `megaDNA` on the evaluated datasets.
+
+## How to Use
+
+The model is compatible with the Hugging Face `transformers` library.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Select the model variant (e.g., vocab size 128 or 32)
+# Replace with the specific repository path if hosted on HF Hub
+hf_model_repository = "vojtam/DNAGPT2_128" 
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    hf_model_repository, 
+    trust_remote_code=True
+).to(device)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    hf_model_repository, 
+    trust_remote_code=True
+)
+
+# Inference Example
+dna_sequence = "ACGTTGCAAACG"
+token_ids = tokenizer.encode(dna_sequence, return_tensors="pt").to(device)
+
+with torch.no_grad():
+    logits = model(token_ids).logits
+
+print(f"Input: {dna_sequence}")
+print(f"Logits shape: {logits.shape}")