OmniGene-4-SFT-v5-merged

Full BF16 model with CPT + SFT v2/v3/v4/v5 + dual-head architecture (3Di + DSSP classifiers)

This is the latest and most capable OmniGene-4 model, merged into a standalone BF16 model. No need to load base Gemma-4 separately.

🎯 What's New in v5

OmniGene-4-SFT-v5 introduces a dual-head architecture:

• Generation head (LM): for natural language tasks (homology, BixBench, knowledge QA)
• 3Di classifier head (per-residue, 20-class): for Foldseek 3D structural alphabet
• DSSP classifier head (per-residue, 8-class): for secondary structure

Joint loss during training: 0.5 × generation_CE + 0.5 × classification_CE

This gives the model two independent inference paths: chat-based generation for unrestricted language, and direct token-level classification for structured biological prediction.

📊 Performance

Latest evaluation (4-bit + Alpaca prompt)

Benchmark	Accuracy
Standard Homology (6,000 pairs)	99.40%
Remote Homology (2,000 pairs)	82.60% ⭐
BixBench Knowledge (T/F)	93.66%

Multi-task generation (6 categories × 100 samples)

Task	Score
Protein	100.0%
Mol	98.0%
Cell	96.6%
Literature	55.9%
Mutation	11.3%
Structure (gen mode)	25.9% char overlap

Classification heads (per-residue, NEW in v5)

Head	Accuracy	vs chance
3Di (20-class)	78.6%	15.7× above chance (5%)
DSSP (8-class)	100.0%	8× above chance (12.5%)

Comparison vs ESM-2 (650M) on identical 500-pair remote homology

OmniGene-4 v5: 82.60% | ESM-2: 50.50% | Gap: +32.1 percentage points

Quick Start

Generation tasks (BF16, 49 GB GPU)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained(
    "dnagpt/OmniGene-4-SFT-v5-merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("dnagpt/OmniGene-4-SFT-v5-merged")

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

### Sequence 1:
MKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQAPILSRVGDGTQDNLSGAEKAVQVKVKALPDAQFEVVHSLAKWKRQTLGQ

### Sequence 2:
MKKFDRGEQVVKVKALPQAQFEEVHSLAKWKRQTLGQHDFSAGEGLYTHMKALRPDEDRLSPLHSVYVDQWDWERVMGDG

### Answer:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=8, do_sample=False)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))

Classification head usage (3Di / DSSP per-residue prediction)

import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download

# Load classification heads
heads_path = hf_hub_download(
    repo_id="dnagpt/OmniGene-4-SFT-v5-merged",
    filename="struct_heads.pt",
)
heads = torch.load(heads_path, map_location="cuda")

head_3di = nn.Linear(2816, 20).to(torch.bfloat16).cuda()
head_dssp = nn.Linear(2816, 8).to(torch.bfloat16).cuda()
head_3di.load_state_dict(heads["head_3di"])
head_dssp.load_state_dict(heads["head_dssp"])

# Predict 3Di / DSSP per-residue
TDI_ALPHABET = list("ACDEFGHIKLMNPQRSTVWY")
DSSP_ALPHABET = list("HBEGITSC")

prompt = "### Instruction:\nPredict 3Di for: MKTAYIAK\n\n### Answer:\n"
ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)

with torch.no_grad():
    out = model(ids, output_hidden_states=True)
    hidden = out.hidden_states[-1][0]  # [seq_len, 2816]

    # For each position after prompt, predict 3Di or DSSP
    for i in range(ids.shape[1] - 5, ids.shape[1]):
        logits_3di = head_3di(hidden[i])
        pred_3di = TDI_ALPHABET[logits_3di.argmax().item()]
        print(f"Position {i}: 3Di={pred_3di}")

4-bit quantization (16 GB GPU)

from transformers import BitsAndBytesConfig

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
)
model = AutoModelForCausalLM.from_pretrained(
    "dnagpt/OmniGene-4-SFT-v5-merged",
    quantization_config=bnb,
    device_map="auto",
)
# struct_heads.pt loaded same as above

🏗️ Training Lineage

OmniGene-4 follows a cumulative training pipeline. Each stage initializes from the previous LoRA + embedding:

Gemma-4-26B-A4B-Instruct-bio (vocab-extended)
    ↓ CPT v2 (32.5 GB mixed corpus, 0.6 epoch, 100 GPU-h)
OmniGene-4-CPT-v2
    ↓ Bio-SFT v2 (179K instructions, 1 epoch, 11.8 GPU-h)
OmniGene-4-SFT-v2
    ↓ Bio-SFT v3 (+20K remote homology pairs, 1 epoch, 13.2 GPU-h)
OmniGene-4-SFT-v3
    ↓ Bio-SFT v4 (Alpaca template + loss masking + task reweighting, 4257 steps, 30 GPU-h)
OmniGene-4-SFT-v4
    ↓ Bio-SFT v5 (dual-head: gen + 3Di + DSSP classifiers, 1350 steps, 5 GPU-h)
OmniGene-4-SFT-v5  ← YOU ARE HERE

This merged model contains all changes from CPT through v5.

🧬 Architecture

• Base: Gemma-4-26B-A4B-Instruct
• Layers: 30 transformer layers
• MoE: 128 experts per layer, top-8 routing
• Active params: ~3.8B per token
• Total params: ~26B
• Vocabulary: 290,048 tokens (262,020 original + 28,028 bio tokens)
• Bio tokens: DNA BPE (20k) + Protein BPE (8k) + 3Di (20) + DSSP (8) + control

📁 Files

• model-*.safetensors (49 GB, 11 shards) — full BF16 weights
• struct_heads.pt (157 KB) — 3Di + DSSP classification heads
• tokenizer.json (36 MB) — extended tokenizer
• bio_sft_v5_meta.json — training metadata
• chat_template.jinja — chat template

🔬 Research Findings

OmniGene-4 v5 demonstrates a Pareto-optimal improvement over previous versions:

• ✅ Maintains v4's Remote Homology breakthrough (82.6%)
• ✅ Restores v3's BixBench performance (93.66%)
• ✅ Adds new token-level structured prediction (3Di 78.6%, DSSP 100%)
• ✅ No regression on any task

Key insight: dual-head architecture allows simultaneous chat-based generation and structured prediction without interference.

🔗 Related Models

• LoRA adapter (1.9 GB, requires base): https://huggingface.co/dnagpt/OmniGene-4-SFT-v5
• v4 LoRA (1.9 GB): https://huggingface.co/dnagpt/OmniGene-4-SFT-v4
• v3 merged BF16: https://huggingface.co/dnagpt/OmniGene-4-SFT-v3-merged
• CPT only: https://huggingface.co/dnagpt/OmniGene-4-CPT-v2-merged
• GGUF Q4_K_M (16 GB): https://huggingface.co/dnagpt/OmniGene-4-SFT-v3-GGUF (v3 only — v5 GGUF coming)

📄 Paper

bioRxiv preprint: https://www.biorxiv.org/content/10.64898/2026.05.12.724542v2
GitHub: https://github.com/maris205/omnigene4

📚 Citation

@article{wang2026omnigene4,
  title={OmniGene-4: A Unified Bio-Language MoE Model with Router-Level Interpretability},
  author={Wang, Liang},
  journal={bioRxiv},
  doi = {10.64898/2026.05.12.724542},
  URL = {https://www.biorxiv.org/content/early/2026/05/19/2026.05.12.724542},
  year={2026}
}

📜 License

Apache 2.0 (inherits from Gemma-4)

📧 Contact

Liang Wang (wangliang.f@gmail.com)
School of Artificial Intelligence and Automation
Huazhong University of Science and Technology