File size: 2,522 Bytes
0bcf67b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | ---
license: apache-2.0
tags:
- pytorch
- deit
- generation
- wip
- prototype
---
# DeiT experiments: Generation
## Overview
A research-oriented **DeiT** prototype targeting **Generation**. The included **nano** setup documents defaults and file formats without presenting unverified performance numbers.
## Quick Check
```bash
python predict.py --help
```
Inspect the script's `__main__` block for its generated smoke-test example. Because this is a custom implementation, generic automatic loading APIs require an explicit adapter before use.
## Contact
Questions or corrections: open a discussion in this repository.
## Training Details
| Item | Value |
|---|---|
| Framework | PyTorch |
| Precision | fp32 |
| Init scheme | xavier uniform |
| Hardware | a single consumer GPU is enough |
| Typical run | minutes, not hours |
## Limitations
The initialization checkpoint has not been trained or audited for robustness, fairness, or domain transfer. The implementation should be treated as an experimental starting point. Results from a future trained checkpoint must be documented separately from the defaults shipped here.
## Default Experiment Recipe
The included configuration uses **sgd** with a **constant warmup** schedule. These are starting values in the script, not evidence of a completed run. For a meaningful evaluation, train all baselines with the same data exposure, tuning budget, and random seeds.
## Architecture
| Item | Value |
|---|---|
| Architecture | DeiT |
| Scale | nano |
| Attention | multi query |
| Fusion | tucker |
| Activation | swish |
| Normalization | scalenorm |
## Repository Status
1. The Python file contains the model and runnable example or training entry point.
2. `config.json` records the generated architecture settings.
3. `training_args.json` records the default experiment recipe.
4. `model.safetensors` is a valid initialization checkpoint for smoke tests; it is **not** presented as a trained benchmark checkpoint.
5. No benchmark score is claimed in this repository.
## Files
| File | Purpose |
|---|---|
| `predict.py` | primary artifact |
| `README.md` | this documentation |
| `config.json` | architecture configuration |
| `training_args.json` | default experiment settings |
| `model.safetensors` | initialization checkpoint |
## License
Released under **apache-2.0**. Review the source-data terms separately when this repository is used with external datasets.
|