---
license: mit
language:
- en
tags:
- synthetic
- pot-o
- tensor-optimization
- proof-of-tensor
- tribewarez
- live-beta
task_categories:
- text-generation
dataset_info:
  features:
  - name: challenge
    dtype: string
  - name: optimal_path
    dtype: string
  - name: difficulty
    dtype: float64
  - name: source
    dtype: string
  splits:
  - name: train
    num_bytes: 16895
    num_examples: 90
  - name: test
    num_bytes: 1877
    num_examples: 10
  download_size: 9497
  dataset_size: 18772
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
---

# synthetic-pot-o-challenges-v1

**Tiny synthetic starter dataset** for training PoT-O (Proof of Tensor Optimizations) pathfinder models.

## Format (JSONL)

```json
{"challenge": "tensor:shape=[32,64];dtype=float16;target_mml=0.42;ops:matmul,lowrank,gelu,quant4,prune0.3,transpose", "optimal_path": "path: matmul[lowrank:16] -> relu -> quant:4bit -> prune:0.35 -> score:0.418"}
```

- **challenge**: Text encoding of tensor properties & allowed operations
- **optimal_path**: Heuristic "good" optimization sequence + predicted score (MML-inspired efficiency)

## Dataset Details

- **Size**: 100 examples (v1 – expand in future versions)
- **Train/Val Split**: 90/10
- **Generation**: Rule-based synthetic (random shapes/dtypes/targets + simple heuristics for paths). Not from real traces yet.
- **Intended use**: Fine-tuning tiny models (e.g. `Tribewarez/pot-o-pathfinder-tiny-v1`) to predict better tensor transformation paths for low-power PoT-O miners.

## Next Iterations

- Add real tensor traces from ai3-lib
- More diverse challenges
- Verified optimal paths via solvers
- 500+ examples with varied op combinations
- Create v2 with real matrix compression benchmarks

---

MIT licensed • Tribewarez guild • Live beta • 2026

## Link to Model

In your model README.yaml add:

```yaml
datasets:
  - Tribewarez/synthetic-pot-o-challenges-v1
```