File size: 1,593 Bytes
33518ee aa90ce0 190d22e d1936ec 85c27af 72da9b3 aa90ce0 a139e23 aa90ce0 c7af953 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 |
---
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 |
|