# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("alainbrown/tiny-gpt", trust_remote_code=True, dtype="auto")Tiny GPT
Tiny GPT is an educational decoder-only Transformer trained from scratch on the TinyStories dataset. The implementation is intentionally small and readable.
Model details
- Architecture: decoder-only causal language model
- Context length: 512 tokens
- Vocabulary size: 10,000
- Hidden size: 256
- Transformer layers: 6
- Attention heads: 8
Source code: https://github.com/alainbrown/tiny-gpt
Usage
This repository contains custom Transformers code. Review it before enabling
trust_remote_code.
from transformers import AutoModelForCausalLM, AutoTokenizer
repo_id = "alainbrown/tiny-gpt"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)
inputs = tokenizer("Once upon a time", return_tensors="pt")
logits = model(**inputs).logits
Intended use
This model is intended for education and experimentation. It is not intended for production, factual question answering, or safety-critical applications.
Limitations
The model is small, trained on synthetic children's stories, and has not been comprehensively evaluated. It may produce incoherent, repetitive, incorrect, or inappropriate text. English is the only supported language.
Training
The training pipeline is available in the linked GitHub repository. This model repository excludes optimizer and progress state and contains inference files only.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alainbrown/tiny-gpt", trust_remote_code=True)