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Model trained on the TinyStories Dataset, see https://arxiv.org/abs/2305.07759
------ EXAMPLE USAGE ---
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model = AutoModelForCausalLM.from_pretrained('roneneldan/TinyStories-1M')
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-1M)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 25.02 |
| ARC (25-shot) | 23.46 |
| HellaSwag (10-shot) | 25.23 |
| MMLU (5-shot) | 24.57 |
| TruthfulQA (0-shot) | 49.4 |
| Winogrande (5-shot) | 52.17 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 0.32 |
|