File size: 2,390 Bytes
84ffcf8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
---

license: apache-2.0
tags:
- pytorch
- beit
- multitask
- prototype
- untrained
---


# Beit for Multitask

## Overview

Working implementation of **Beit** for **Multitask** using a **base** configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted.

The whole setup fits in a single module on purpose.

## Architecture

1. **Architecture**: Beit
2. **Scale**: base
3. **Attention**: grouped query
4. **Fusion**: low rank
5. **Activation**: gelu tanh
6. **Normalization**: layernorm

## Limitations

The checkpoint is untrained and unaudited. Treat the repository as an experimental starting point; anything learned from a future trained version must be documented separately.

## Environment

1. **Python**: >= 3.9
2. **torch**: 2.x
3. **safetensors**: any recent
4. **transformers**: optional, for loaders

## 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.

## Quick check

```bash

python model.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.

## Default experiment recipe

The included configuration uses **sgd** with a **exponential** 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.

## Files

| File | Purpose |
|---|---|
| `model.py` | primary artifact |
| `README.md` | this documentation |
| `config.json` | architecture configuration |
| `training_args.json` | default experiment settings |
| `model.safetensors` | initialization checkpoint |
| `NOTES.md` | informal to-do notes |
| `examples/sample_usage.py` | usage sketch |

## License

Released under **apache-2.0**. Review the source-data terms separately when this repository is used with external datasets.