OmniGene-4-SFT-v5 (LoRA + Classification Heads)

LoRA adapter + extended embedding + dual-head classifiers for OmniGene-4. Requires base Gemma-4-26B-A4B-it-bio.

This is the LoRA-only version — for a standalone BF16 model with weights merged, see OmniGene-4-SFT-v5-merged.

What's in this repo (~1.9 GB)

File	Size	Purpose
lora_weights.pt	306 MB	LoRA delta (cumulative: CPT v2 + SFT v2/v3/v4/v5)
embedding_weights.pt	1.6 GB	Extended embedding (290,048 tokens including 28,028 bio tokens)
struct_heads.pt	157 KB	3Di (20-class) + DSSP (8-class) per-residue heads
tokenizer.json	36 MB	Extended bio tokenizer
chat_template.jinja	17 KB	Alpaca-style template
bio_sft_v5_meta.json	—	Training metadata

Performance (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%
3Di per-residue (head, 20-class)	78.6%
DSSP per-residue (head, 8-class)	100.0%

vs ESM-2 (650M) on identical 500-pair remote homology: ESM-2 50.5% — gap +32.1 pp.

Quick Start

import torch
import torch.nn as nn
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import LoraConfig, inject_adapter_in_model
from huggingface_hub import hf_hub_download

BASE = "dnagpt/gemma-4-26B-A4B-it-bio"
ADAPTER = "dnagpt/OmniGene-4-SFT-v5"

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(BASE, quantization_config=bnb, device_map={"": 0})
tokenizer = AutoTokenizer.from_pretrained(ADAPTER)

# Inject LoRA
lora_config = LoraConfig(
    r=64, lora_alpha=128, lora_dropout=0.0, bias="none",
    target_modules=['q_proj','k_proj','v_proj','o_proj',
                    'gate_proj','up_proj','down_proj','router.proj'],
)
inject_adapter_in_model(lora_config, model.model.language_model, adapter_name="default")

# Load v5 LoRA + embedding
lora_path = hf_hub_download(ADAPTER, "lora_weights.pt")
embed_path = hf_hub_download(ADAPTER, "embedding_weights.pt")
heads_path = hf_hub_download(ADAPTER, "struct_heads.pt")

ms = model.state_dict()
for k, v in torch.load(lora_path, map_location="cpu").items():
    if k in ms: ms[k].copy_(v)
model.get_input_embeddings().weight.data.copy_(torch.load(embed_path, map_location="cpu"))
model.eval()

# Optional: classification heads
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"])

Training Lineage

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

Citation

@article{wang2026omnigene4,
  title={OmniGene-4: A Unified Bio-Language MoE Model with Router-Level Interpretability},
  author={Wang, Liang},
  journal={bioRxiv},
  year={2026}
}

Contact

Liang Wang (wangliang.f@gmail.com) — Huazhong University of Science and Technology