# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BreadAi/gpt-YA-1-1_70M")
model = AutoModelForCausalLM.from_pretrained("BreadAi/gpt-YA-1-1_70M", device_map="auto")Quick Links
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 25.64 |
| ARC (25-shot) | 22.53 |
| HellaSwag (10-shot) | 27.37 |
| MMLU (5-shot) | 25.38 |
| TruthfulQA (0-shot) | 47.09 |
| Winogrande (5-shot) | 50.91 |
| GSM8K (5-shot) | 0.0 |
| DROP (3-shot) | 6.18 |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="BreadAi/gpt-YA-1-1_70M")