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Model: gvadhul/gpt2-2layer-1m
Source: Original Platform

README.md

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+---
+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.