Tiny Model Weights (safetensors)
A genuinely-valid safetensors file with two small float32 tensors (36 parameters total) — an 8×4 weight and a length-4 bias. The values are meaningless sample data, not a trained model; a fixture for testing safetensors loaders and weight inspectors.
| tensor | dtype | shape | params |
|---|---|---|---|
| embedding.weight | F32 | [8, 4] | 32 |
| dense.bias | F32 | [4] | 4 |
Specifications
- Tensors
- 2
- Parameters
- 36
- Dtype
- float32
- Format
- safetensors
- Note
- sample-only, not a trained model
What is a .safetensors file?
safetensors is a simple, safe binary format for storing model weights. An 8-byte length precedes a JSON header that maps each tensor name to its dtype, shape, and byte offsets, followed by the raw tensor data — with no executable code, unlike Python pickles. It has become a common format for sharing model checkpoints.
How to use this file
Use an example safetensors file to test weight loaders and inspectors, header parsing, and conversion to or from other checkpoint formats. This sample holds meaningless values, not a trained model.
How to use this file for testing
“Tiny Model Weights (safetensors)” is a deterministic Novus Examples fixture for ML training data, Conversion testing. Labelled, synthetic datasets in the shapes ML pipelines expect — JSONL for text tasks, image annotations, embeddings, and sample weights — for testing data loaders, tokenizers, and training tooling.
Documented properties for this file: safetensors. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.
Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.
AI/ML fixtures are fully synthetic with documented schemas — no real people or data. Test data loaders, tokenizers, annotation converters, embedding/vector stores, or eval-metric parsers against the known structure and fixed seeds.
These are labelled, training-shaped fixtures with a documented schema. Test your data loader, tokenizer, or format converter against it; every label and value is synthetic.
Related files
- jsonDetection Annotations — COCO (JSON)Object-detection annotations for the scene in the COCO JSON format — images, categories, and per-object bounding boxes as [x, y, width, height]. Grouped with YOLO and Pascal-VOC twins for testing annotation-format conversion.
- xmlDetection Annotations — Pascal VOC (XML)The same detection boxes in the Pascal VOC XML format — a per-image annotation with size, and one object element per box with pixel corner coordinates. The XML twin of the COCO and YOLO annotations.
- xmlDetection Annotations — Warehouse Pascal VOC (XML)Pascal VOC XML annotations for the warehouse detection scene.
- txtDetection Annotations — YOLO (TXT)The same detection boxes in the YOLO text format — one object per line as class id and box centre, width, and height normalised to 0–1. The format twin of the COCO and VOC annotations.
- pngObject-detection Scene (PNG, 640×480)A simple rendered street scene with a person, a car, and a tree at known pixel coordinates — the image the COCO, YOLO, and Pascal-VOC annotation twins describe. A fixture for testing object-detection loaders and annotation converters.
- jsonText Embeddings — 16-dim (JSON)A set of 24 L2-normalised 16-dimensional text embeddings as JSON — each record pairs an id and its source text with a float vector. A fixture for testing vector stores, similarity search, and embedding loaders. Parquet and .npy twins included.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.