Instructions to use dnagpt/OmniGene-4-SFT-v3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dnagpt/OmniGene-4-SFT-v3-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dnagpt/OmniGene-4-SFT-v3-GGUF",
	filename="OmniGene-4-SFT-v3-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-SFT-v3-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-SFT-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dnagpt/OmniGene-4-SFT-v3-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-SFT-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf dnagpt/OmniGene-4-SFT-v3-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-SFT-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf dnagpt/OmniGene-4-SFT-v3-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-SFT-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dnagpt/OmniGene-4-SFT-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/dnagpt/OmniGene-4-SFT-v3-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use dnagpt/OmniGene-4-SFT-v3-GGUF with Ollama:
```
ollama run hf.co/dnagpt/OmniGene-4-SFT-v3-GGUF:Q4_K_M
```

Unsloth Studio

How to use dnagpt/OmniGene-4-SFT-v3-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-SFT-v3-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-SFT-v3-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-SFT-v3-GGUF to start chatting

How to use dnagpt/OmniGene-4-SFT-v3-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-SFT-v3-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-SFT-v3-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use dnagpt/OmniGene-4-SFT-v3-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-SFT-v3-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-SFT-v3-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use dnagpt/OmniGene-4-SFT-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/dnagpt/OmniGene-4-SFT-v3-GGUF:Q4_K_M
```

Lemonade

How to use dnagpt/OmniGene-4-SFT-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dnagpt/OmniGene-4-SFT-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OmniGene-4-SFT-v3-GGUF-Q4_K_M

List all available models

lemonade list

OmniGene-4-SFT-v3-GGUF

GGUF format models for OmniGene-4-SFT-v3 (instruction-tuned version)

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.

Performance

Benchmark	Accuracy
Standard Homology	99.95% (6,000 pairs)
Remote Homology	59.50% (2,000 pairs)
BixBench Knowledge	93.66%

vs. ESM-2 (650M): OmniGene-4 59.5% vs ESM-2 50.5% (+9 pp on same 2,000 pairs)

Available Quantizations

Quantization	File	Size	RAM Required	Quality
F16	`OmniGene-4-SFT-v3-f16.gguf`	50.6 GB	~52 GB	Best quality
Q4_K_M	`OmniGene-4-SFT-v3-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-SFT-v3-Q4_K_M.gguf",
    n_ctx=4096,
    n_gpu_layers=-1,  # Offload all layers to GPU
)

# Example: Protein homology detection
prompt = """### Instruction:
Determine if the two protein sequences below are structurally related (homologous).

### Sequence 1:
MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDEDRLELEWHQALLRGEMPQTIGGGIGQSRLTMLLLQLPHIGQVQAGVWPAAVRESVPSLL

### Sequence 2:
MKKFDRGEQVVKVKALPQAQFEEVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDEDRLELEWHQALLRGEMPQTIGGGIGQSRLTMLLLQLPHIGQVQAGVWPAAVRESVPSLL

### Answer:
"""

output = llm(prompt, max_tokens=5, temperature=0)
print(output['choices'][0]['text'])  # Expected: "Yes" or "No"

Option 2: llama.cpp Command Line

./llama-cli -m OmniGene-4-SFT-v3-Q4_K_M.gguf \
    -p "### Instruction:\nDetermine if the two protein sequences are homologous.\n### Sequence 1:\nMKTAYIAK...\n### Answer:\n" \
    -n 5 -ngl -1

Option 3: Ollama

# Create Modelfile
cat > Modelfile <<EOF
FROM ./OmniGene-4-SFT-v3-Q4_K_M.gguf

TEMPLATE """### Instruction:
{{ .System }}

{{ .Prompt }}

### Answer:
"""
EOF

ollama create omnigene-4-sft -f Modelfile
ollama run omnigene-4-sft

Option 4: LM Studio

Download OmniGene-4-SFT-v3-Q4_K_M.gguf
Place in LM Studio models folder
Load in LM Studio
Use the instruction template below

Supported Tasks

The model is instruction-tuned on 8 task families:

Task	Examples	Proportion
Protein homology	49,894	25.0%
Literature (UniProtQA)	39,915	20.0%
Mutation (MutaDescribe)	29,936	15.0%
Cell biology	29,936	15.0%
Molecule (SMILES)	25,945	13.0%
Structure (3D)	19,958	10.0%
DNA homology	3,992	2.0%

Example Tasks

1. Protein Homology Detection

### Instruction:
Determine if the two protein sequences below are structurally related (homologous).

### Sequence 1:
[protein sequence 1]

### Sequence 2:
[protein sequence 2]

### Answer:

2. Protein Function Prediction

### Instruction:
Predict the biological function of the following protein sequence.

### Protein Sequence:
[protein sequence]

### Answer:

3. Mutation Effect Prediction

### Instruction:
Describe the effect of the mutation on protein function.

### Wild-type:
[wild-type sequence]

### Mutant:
[mutant sequence]

### Answer:

4. Cell Type Identification

### Instruction:
Identify the cell type based on the gene expression profile.

### Gene Expression:
CD4: high, CD8: low, IL2: high

### Answer:

5. SMILES to Properties

### Instruction:
Predict the drug-likeness of the following molecule.

### SMILES:
CC(C)Cc1ccc(cc1)C(C)C(O)=O

### Answer:

Model Description

OmniGene-4-SFT-v3 is the final instruction-tuned biological foundation model with:

Base: Gemma-4-26B-A4B-Instruct + Bio CPT + Bio SFT
Vocabulary: 290,048 tokens
SFT data: 199,576 instruction examples
Precision: GGUF F16 / Q4_K_M

Other Versions

Full BF16 (HuggingFace transformers): https://huggingface.co/dnagpt/OmniGene-4-SFT-v3-merged
LoRA adapter (requires base model): https://huggingface.co/dnagpt/OmniGene-4-SFT-v3
4-bit auto-quantize: https://huggingface.co/dnagpt/OmniGene-4-SFT-v3-4bit
CPT only GGUF: https://huggingface.co/dnagpt/OmniGene-4-CPT-v2-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

Downloads last month: 177

GGUF

Model size

25B params

Architecture

gemma4

Hardware compatibility

4-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for dnagpt/OmniGene-4-SFT-v3-GGUF

Base model

dnagpt/OmniGene-4-SFT-v3-merged

Quantized

(1)

this model