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

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

./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

Download OmniGene-4-CPT-v2-Q4_K_M.gguf
Place in LM Studio models folder
Load in LM Studio
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

dnagpt/OmniGene-4-CPT-v2-GGUF

OmniGene-4-CPT-v2-GGUF