✔️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%}")
You can’t. What you can do, though, is use WhatsApp’s and Telegram’s web platforms to transfer stickers. It’s easy, but might take a while.Open WhatsApp in your browser, find a sticker you like in a chat, and right-click on it to save it as an image. The file won’t be a picture, though—it’s a webpage and will have a .webp extension. Don’t be scared, this is the way. Repeat this step to save as many stickers as you want.Then, open Telegram in your browser and go into your Saved messages chat. Just as you’d share a file with a friend, click the Share file button on the bottom left of the chat window (it looks like a dog-eared paper), and select the .webp files you downloaded. Click Open and you’ll see your stickers in your Saved messages chat. This is now your sticker depository. To use them, forward them as you would a message from one chat to the other: by clicking or long-pressing on the sticker, and then choosing Forward.
The SSE was the first modern stock exchange to open in China, with trading commencing in 1990. It has now grown to become the largest stock exchange in Asia and the third-largest in the world by market capitalization, which stood at RMB 50.6 trillion (US$7.8 trillion) as of September 2021. Stocks (both A-shares and B-shares), bonds, funds, and derivatives are traded on the exchange. The SEE has two trading boards, the Main Board and the Science and Technology Innovation Board, the latter more commonly known as the STAR Market. The Main Board mainly hosts large, well-established Chinese companies and lists both A-shares and B-shares.
