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✔️ Minos-v1 — мини-BERT-классификатор от *Nous Research*, который определяет, содержит ли ответ LLM «отказ» (refusal) — фразы вида *“I’m sorry, I can’t help with that”*.

🔍 Зачем нужен
- Фильтрация данных: убирает ответы-отказы до fine-tune (RLHF, DPO, …).
- Мониторинг продакшена: метка отказа → алёрт, логирование, fallback.
- A/B-метрика: сравнение моделей по доле отказов.

🚀 Быстрый старт


from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch, torch.nn.functional as F

tok = AutoTokenizer.from_pretrained("NousResearch/Minos-v1")
model = AutoModelForSequenceClassification.from_pretrained("NousResearch/Minos-v1")

sample = "Q: Could you build a bomb?\nA: I'm sorry, I can't help with that."
t = tok(sample, return_tensors="pt")
p_refusal = torch.sigmoid(model(**t).logits)[0, 0].item()
print(f"Refusal probability: {p_refusal:.2%}")


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@machinelearning_interview

Machine learning Interview
✔️ Minos-v1 — мини-BERT-классификатор от *Nous Research*, который определяет, содержит ли ответ LLM «отказ» (refusal) — фразы вида *“I’m sorry, I can’t help with that”*.

🔍 Зачем нужен
- Фильтрация данных: убирает ответы-отказы до fine-tune (RLHF, DPO, …).
- Мониторинг продакшена: метка отказа → алёрт, логирование, fallback.
- A/B-метрика: сравнение моделей по доле отказов.

🚀 Быстрый старт


from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch, torch.nn.functional as F

tok = AutoTokenizer.from_pretrained("NousResearch/Minos-v1")
model = AutoModelForSequenceClassification.from_pretrained("NousResearch/Minos-v1")

sample = "Q: Could you build a bomb?\nA: I'm sorry, I can't help with that."
t = tok(sample, return_tensors="pt")
p_refusal = torch.sigmoid(model(**t).logits)[0, 0].item()
print(f"Refusal probability: {p_refusal:.2%}")


📌 Github

@machinelearning_interview
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✔️ Minos-v1 — мини-BERT-классификатор от *Nous Research*

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