# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ccore/opt-125-smart-test")
model = AutoModelForCausalLM.from_pretrained("ccore/opt-125-smart-test")Quick Links
hf-causal (pretrained=ccore/opt-125-smart-test), limit: None, provide_description: False, num_fewshot: 0, batch_size: None
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| openbookqa | 0 | acc | 0.1560 | ± | 0.0162 |
| acc_norm | 0.3420 | ± | 0.0212 | ||
| piqa | 0 | acc | 0.6197 | ± | 0.0113 |
| acc_norm | 0.6023 | ± | 0.0114 | ||
| prompt format: |
[INSTRUCTION] what's the capital of Brasil? ?
[RESPONSE] The capital of Brazil is Brasilia
datasets: OpenOrca, Wizard dataset, custom papers data .
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ccore/opt-125-smart-test")