Kai9987kai/supermix-omni-collective-v7-frontier

Supermix Omni Collective V7 Frontier

Custom PyTorch checkpoint for the omni_collective_v7 frontier model.

Included files

• omni_collective_v7_frontier.pth
• omni_collective_v7_frontier_meta.json
• omni_collective_v7_frontier_summary.json
• omni_collective_v7_model.py
• omni_collective_v5_model.py
• omni_collective_v4_model.py
• omni_collective_model.py
• image_feature_utils.py
• image_recognition_model.py
• math_equation_model.py
• protein_folding_model.py
• train_omni_collective_v7.py

Model summary

• Parameters: 77560031
• Stage 1 rows: 28076
• Stage 2 rows: 28254
• Stage 2 validation score: 0.4115
• Stage 2 intent accuracy: 0.7280
• Stage 2 response accuracy: 0.1072
• Stage 2 vision accuracy: 0.5385
• Stage 2 domain accuracy: 0.6844

Training sources

• all-model distillation rows: 133
• v33 benchmax rows: 1788
• v39 benchmax rows: 2047
• conversation supermix plus v7: 2600
• conversation creative v7: 1400
• conversation reasoning v7: 1320
• conversation books v7: 920
• conversation science v7: 240
• conversation science novel v7: 200
• conversation coding v7: 380
• math exact v7 added: 548
• protein folding v7 added: 120
• protein pack v7: 120
• science image: 430
• video contact: 248

Notes

This is a custom checkpoint, not a standard Transformers from_pretrained model.

v7 extends the omni line with:

• all-model distillation across the local Supermix model families
• longer multi-pass deliberation with grounded-response guards
• broader conversation-focused continuation data
• extra math and protein-folding supervision
• preservation of text, vision, reasoning, math, and specialist-profile behavior in one checkpoint

The teacher league for this run included:

• v40_benchmax
• qwen_v28
• qwen_v30
• omni_collective_v1 through omni_collective_v6
• specialist models including math_equation_micro_v1, protein_folding_micro_v1, and science_vision_micro_v1

Minimal local usage

from pathlib import Path
from omni_collective_v7_model import OmniCollectiveEngineV7

engine = OmniCollectiveEngineV7(
    weights_path=Path("omni_collective_v7_frontier.pth"),
    meta_path=Path("omni_collective_v7_frontier_meta.json"),
)

print(engine.answer("Give a grounded summary of what hydrophobic collapse does during protein folding."))