Instructions to use dnagpt/OmniGene-4-CPT-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dnagpt/OmniGene-4-CPT-v2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dnagpt/OmniGene-4-CPT-v2-GGUF",
	filename="OmniGene-4-CPT-v2-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dnagpt/OmniGene-4-CPT-v2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use dnagpt/OmniGene-4-CPT-v2-GGUF with Ollama:
```
ollama run hf.co/dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M
```

Unsloth Studio

How to use dnagpt/OmniGene-4-CPT-v2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dnagpt/OmniGene-4-CPT-v2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for dnagpt/OmniGene-4-CPT-v2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dnagpt/OmniGene-4-CPT-v2-GGUF to start chatting

How to use dnagpt/OmniGene-4-CPT-v2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dnagpt/OmniGene-4-CPT-v2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use dnagpt/OmniGene-4-CPT-v2-GGUF with Docker Model Runner:
```
docker model run hf.co/dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M
```

Lemonade

How to use dnagpt/OmniGene-4-CPT-v2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dnagpt/OmniGene-4-CPT-v2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OmniGene-4-CPT-v2-GGUF-Q4_K_M

List all available models

lemonade list

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

pip install llama-cpp-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

# 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

@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

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GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

4-bit

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Model tree for dnagpt/OmniGene-4-CPT-v2-GGUF

Base model

dnagpt/OmniGene-4-CPT-v2-merged

Quantized

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