OmniGene-4-SFT-v3-4bit

Instruction-tuned model with automatic 4-bit quantization for RTX 5090 (32GB)

This model automatically quantizes to 4-bit when loaded, requiring only ~13GB GPU memory.

Model Description

OmniGene-4-SFT-v3-4bit 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 across 8 task families
• Storage: BF16 (~49 GB, 32 shards of ~1.5GB each)
• Runtime: Automatic 4-bit quantization (~13GB GPU memory)

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)

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model (automatically quantizes to 4-bit)
model = AutoModelForCausalLM.from_pretrained(
    "dnagpt/OmniGene-4-SFT-v3-4bit",
    device_map="auto",  # Automatically applies quantization_config.json
)
tokenizer = AutoTokenizer.from_pretrained("dnagpt/OmniGene-4-SFT-v3-4bit")

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

### Sequence 1:
MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDEDRLELEWHQALLRGEMPQTIGGGIGQSRLTMLLLQLPHIGQVQAGVWPAAVRESVPSLL

### Sequence 2:
MKKFDRGEQVVKVKALPQAQFEEVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDGERQFSTLKSTVEAIWAGIKATEAAVSEEFGLAPFLPDQIHFVHSQELLSRYPDLDAKGRERAIAKDLGAVFLVGIGGKLSDGHRHDVRAPDYDDWSTPSELGHAGLNGDILVWNPVLEDAFELSSMGIRVDADTLKHQLALTGDEDRLELEWHQALLRGEMPQTIGGGIGQSRLTMLLLQLPHIGQVQAGVWPAAVRESVPSLL

### Answer:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=5, do_sample=False)
answer = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
print(answer)  # Expected: "Yes" or "No"

Hardware Requirements

• GPU Memory: ~13-15GB (after automatic 4-bit quantization)
• Recommended: RTX 5090 (32GB), RTX 4090 (24GB), or better
• Minimum: RTX 3090 (24GB)

Quantization Details

This model uses bitsandbytes NF4 quantization with double quantization:

• Method: NF4 (Normal Float 4-bit)
• Compute dtype: bfloat16
• Double quantization: Yes
• Quality: Minimal accuracy loss compared to BF16

The quantization happens automatically when you load the model thanks to the included quantization_config.json.

Download Size vs Runtime Size

• Download: ~49GB (BF16 weights, 32 shards)
• Disk: ~49GB
• GPU Memory: ~13GB (after automatic quantization)

The model is stored in BF16 for maximum quality, then quantized to 4-bit at load time.

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

prompt = """### 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

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

### Protein Sequence:
[protein sequence]

### Answer:
"""

3. Mutation Effect Prediction

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

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

### Mutant:
[mutant sequence]

### Answer:
"""

4. Cell Type Identification

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

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

### Answer:
"""

5. SMILES to Properties

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

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

### Answer:
"""

Model Architecture

• Layers: 30 transformer layers
• Experts: 128 experts per layer (top-8 routing)
• Hidden size: 2816
• Attention heads: 22
• Active parameters: ~3.8B per token
• Total parameters: ~26B

Other Versions

• Full BF16 (no quantization): https://huggingface.co/dnagpt/OmniGene-4-SFT-v3-merged
• LoRA adapter (requires base model): https://huggingface.co/dnagpt/OmniGene-4-SFT-v3
• CPT only: https://huggingface.co/dnagpt/OmniGene-4-CPT-v2-4bit

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