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
Detailed results can be found here
| 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 |