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auryn-macmillan
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auryn-macmillan
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| Benchmark | BoostedV1 (phase-1) | BoostedV1-ILv9 |
|---|---|---|
| GSM8K | 46.0% | 46.5% |
| HumanEval (pass@1) | 7.3% | 11.0% |
The IL v9 loop produced the largest gains on code generation (HumanEval pass@1 7.3% -> 11.0%).
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained('auryn-macmillan/boostedv1-ilv9') tok = AutoTokenizer.from_pretrained('auryn-macmillan/boostedv1-ilv9') inputs = tok('Write Python to check if a number is prime.', return_tensors='pt') out = model.generate(**inputs, max_new_tokens=256) print(tok.decode(out[0]))