---
license: unknown
tags:
- pytorch
- perceiver
- generation
- wip
---

# Generation (Perceiver)

## Overview

This is an experimental **Perceiver** codebase for **Generation**. It keeps the **giant** setup intentionally manageable so architecture changes can be inspected before a full training run.

Feedback on the structure is welcome; the code is intentionally short.

---

## Default Experiment Recipe

The generated recipe uses rmsprop with a cosine schedule.

These values exist so the script runs end-to-end. A meaningful experiment needs its own tuning budget and at least three seeds.

---

## Limitations

* weights are random-init, so outputs carry no signal
* robustness, fairness and domain transfer are untested
* the API may change without notice while the design settles

---

## Repository Status

* Single-file implementation; the `__main__` block runs a smoke test.
* `config.json` and `training_args.json` hold the generated settings.
* `model.safetensors` is freshly initialized — plumbing, not results.
* No benchmark numbers are reported on purpose.

---

## Training Details

* Weights initialized with a kaiming normal scheme.
* Runs on a single consumer GPU.
* Full smoke test takes minutes, not hours.
* No distributed setup required.

---

## Changelog

* 2026-10-18 — initial upload
* 2026-10-19 — config and docs sync

---

## Evaluation Guidance

A first evaluation could use a task-specific held-out set. Report the metric across at least three seeds and include a matched-capacity baseline; keep logs and library versions with any published number.

---

## Files

* `eval.py` — primary artifact
* `README.md` — this documentation
* `config.json` — architecture configuration
* `training_args.json` — default experiment settings
* `model.safetensors` — initialization checkpoint
* `USE_POLICY.md` — use and redistribution notes

---

## License

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