---
language:
- en
license: apache-2.0
tags:
- gpt2
- causal-lm
- text-generation
- tiny
- testing
pipeline_tag: text-generation
---

# gpt2-2layer-1m

A **randomly-initialized**, 2-layer GPT-2 model for functional / integration testing.
It produces incoherent text — that is by design.

## Architecture

| Hyperparameter | Value |
|---|---|
| Architecture | GPT-2 (decoder-only) |
| Layers | 2 |
| Hidden size | 256 |
| Attention heads | 4 |
| FFN inner size | 1 024 |
| Context length | 512 |
| Vocabulary size | 183 (byte-level BPE) |
| Total params | ~1.76 M |
| Non-embedding params | ~1.58 M |

## Usage

```python
from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast

tokenizer = PreTrainedTokenizerFast.from_pretrained("gvadhul/gpt2-2layer-1m")
model     = GPT2LMHeadModel.from_pretrained("gvadhul/gpt2-2layer-1m")

inputs = tokenizer("Hello world", return_tensors="pt")
out    = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(out[0]))
```

## Intended use
Functional / unit testing of pipelines that need a *tiny* causal LM.
**Not suitable for any real NLP task** — weights are random.