Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,128 @@
+---
+license: apache-2.0
+language:
+- en
+tags:
+- biology
+- protein
+- bioinformatics
+- mixture-of-experts
+- gguf
+- llama.cpp
+base_model: dnagpt/OmniGene-4-CPT-v2-merged
+quantized_by: Liang Wang
+---
+
+# OmniGene-4-CPT-v2-GGUF
+
+**GGUF format models for OmniGene-4-CPT-v2** (continued pretraining checkpoint)
+
+GGUF format quantized versions of OmniGene-4 for efficient inference on consumer GPUs and CPUs using llama.cpp, llama-cpp-python, Ollama, LM Studio, and other GGUF-compatible runtimes.
+
+## Available Quantizations
+
+| Quantization | File | Size | RAM Required | Quality |
+|---|---|---|---|---|
+| **F16** | `OmniGene-4-CPT-v2-f16.gguf` | 50.6 GB | ~52 GB | Best quality |
+| **Q4_K_M** | `OmniGene-4-CPT-v2-Q4_K_M.gguf` | 16 GB | ~17 GB | Recommended balance |
+
+## Hardware Requirements
+
+| Quantization | GPU | CPU + RAM |
+|---|---|---|
+| **F16** | RTX A6000 (48GB) | 64GB+ system RAM |
+| **Q4_K_M** | RTX 5090 (32GB) / RTX 4090 (24GB) / RTX 3090 (24GB) | 32GB+ system RAM |
+
+## Quick Start
+
+### Option 1: llama-cpp-python
+
+```bash
+pip install llama-cpp-python
+```
+
+```python
+from llama_cpp import Llama
+
+llm = Llama(
+    model_path="OmniGene-4-CPT-v2-Q4_K_M.gguf",
+    n_ctx=4096,
+    n_gpu_layers=-1,  # Offload all layers to GPU
+)
+
+output = llm("MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEK", max_tokens=100)
+print(output['choices'][0]['text'])
+```
+
+### Option 2: llama.cpp Command Line
+
+```bash
+./llama-cli -m OmniGene-4-CPT-v2-Q4_K_M.gguf -p "MKTAYIAKQRQISFVKSHFSRQLEERL" -n 100 -ngl -1
+```
+
+### Option 3: Ollama
+
+```bash
+# Create Modelfile
+cat > Modelfile <<EOF
+FROM ./OmniGene-4-CPT-v2-Q4_K_M.gguf
+EOF
+
+ollama create omnigene-4-cpt -f Modelfile
+ollama run omnigene-4-cpt
+```
+
+### Option 4: LM Studio
+
+1. Download `OmniGene-4-CPT-v2-Q4_K_M.gguf`
+2. Place in LM Studio models folder
+3. Load in LM Studio
+4. Start chatting
+
+## Model Description
+
+OmniGene-4-CPT-v2 is a biological foundation model with:
+- **Base**: Gemma-4-26B-A4B-Instruct (MoE, 128 experts, top-8 routing)
+- **Vocabulary**: 290,048 tokens (262,020 original + 28,028 bio tokens)
+- **CPT data**: 32.5 GB mixed corpus (DNA, Protein, OpenWebText, Structure)
+- **Training**: 0.6 epoch, 2,806 steps, 8×H20 GPUs
+
+## Biological Tokens
+
+The model includes 28,028 additional biological tokens:
+- **DNA BPE**: 20,000 tokens (optimized for genomic sequences)
+- **Protein BPE**: 8,000 tokens (optimized for amino acid sequences)
+- **3Di alphabet**: 20 tokens (Foldseek structural alphabet)
+- **DSSP**: 8 tokens (secondary structure: H, E, C, etc.)
+
+## Other Versions
+
+- **Full BF16** (HuggingFace transformers): https://huggingface.co/dnagpt/OmniGene-4-CPT-v2-merged
+- **LoRA adapter** (requires base model): https://huggingface.co/dnagpt/OmniGene-4-CPT-v2
+- **4-bit auto-quantize**: https://huggingface.co/dnagpt/OmniGene-4-CPT-v2-4bit
+- **Instruction-tuned GGUF**: https://huggingface.co/dnagpt/OmniGene-4-SFT-v3-GGUF
+
+## Citation
+
+```bibtex
+@article{wang2026omnigene4,
+  title={OmniGene-4: A Unified Bio-Language MoE Model with Router-Level Interpretability},
+  author={Wang, Liang},
+  journal={bioRxiv},
+  year={2026}
+}
+```
+
+## Paper
+
+Full paper: https://github.com/maris205/omnigene4
+
+## License
+
+Apache 2.0
+
+## Contact
+
+Liang Wang (wangliang.f@gmail.com)  
+School of Artificial Intelligence and Automation  
+Huazhong University of Science and Technology