✔️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%}")
✔️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%}")
Telegram is riding high, adding tens of million of users this year. Now the bill is coming due.Telegram is one of the few significant social-media challengers to Facebook Inc., FB -1.90% on a trajectory toward one billion users active each month by the end of 2022, up from roughly 550 million today.
The global forecast for the Asian markets is murky following recent volatility, with crude oil prices providing support in what has been an otherwise tough month. The European markets were down and the U.S. bourses were mixed and flat and the Asian markets figure to split the difference.The TSE finished modestly lower on Friday following losses from the financial shares and property stocks.For the day, the index sank 15.09 points or 0.49 percent to finish at 3,061.35 after trading between 3,057.84 and 3,089.78. Volume was 1.39 billion shares worth 1.30 billion Singapore dollars. There were 285 decliners and 184 gainers.
