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license: other
library_name: pytorch
tags:
- supermix
- multimodal
- pytorch
- custom-model
- reasoning
- vision
- math
- protein-folding
- grounded-generation
- experimental
---
# 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
```python
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."))
```
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