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.
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.
- 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.
- parquetText Embeddings — 16-dim (Parquet)The same 16-dimensional embeddings as Apache Parquet — id and text columns plus one column per dimension. The columnar twin, for testing analytics engines and Parquet-based vector pipelines.
Generated by generation/ai_datasets.py. Free for any use, no attribution required — license.