---
datasets:
- roneneldan/TinyStories
---
Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759

Based on GPT-Neo architecture.

License: mit

---
hyperparams used to train this model:

lr = 5e-4,
lr_schedule = constant, 
wd=0.1,
adam_beta1=0.9, adam_beta2 = 0.95,
context_length=512,
batch_size=80,
gradient_accumulation_steps=16

------ EXAMPLE USAGE ---

from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig

model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-33M')

tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-125M")

prompt = "Once upon a time there was"

input_ids = tokenizer.encode(prompt, return_tensors="pt")

# Generate completion
output = model.generate(input_ids, max_length = 1000, num_beams=1)

# Decode the completion
output_text = tokenizer.decode(output[0], skip_special_tokens=True)

# Print the generated text
print(output_text)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_roneneldan__TinyStories-33M)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 24.38   |
| ARC (25-shot)         | 24.23          |
| HellaSwag (10-shot)   | 25.69    |
| MMLU (5-shot)         | 23.82         |
| TruthfulQA (0-shot)   | 47.64   |
| Winogrande (5-shot)   | 49.09   |
| GSM8K (5-shot)        | 0.0        |
| DROP (3-shot)         | 0.19         |