Machine Learning / AI

import unittest class Solution: def isvalid(self, s:str) -> bool: matching_brackets = { ")": "(", "]": "[", "}": "{", } stack: list[str] = [] for bracket in s: if bracket in "([{": stack.append(bracket) continue if bracket not in matching_brackets: return False if not stack or stack[-1] != matching_brackets[bracket]: return False stack.pop() return not stack class SolutionTest: def __init__(self): self.Solution = Solution() def run(self): test_cases = [ ("", True), ("()", True), ("(){}[]", True), ("(({}))", True), ("(", False), (")", False), ("(((", False), ("{[]}", False), ] for brackets, expected in test_cases: actual = self.Solution.isvalid(brackets) assert actual == expected, ( f"Xatolik uchun {brackets!r}" f"Kutilgan {expected}, lekin olingan {actual}" ) print(f"Barcha testlar muvaffaqiyatli o'tdi: {len(test_cases)}") if __name__ == "__main__": SolutionTest().run()

Foydalanuvchi xabarining LLM orqali inferensiya jarayonidagi bosqichma-bosqich yo'lini tavsiflang — chatdan javobgacha, Transformer arxitekturasi tafsilotlarini ham o'z ichiga olgan holda.

Har kuni qanday AI-anjomalaridan foydalanasiz?

Nima uchun aniq foydalanuvchi so'rovlarini kesh qilish odatda cheklangan biznes qiymatga ega va siz aynan nima nazarda tutgansiz javoblarni kesh qilish bilan?

RAG tizimida qidirish / to'liq javob tsikli qancha davom etdi?

Loyihalaringiz haqida gapiring: aniq nima qildingiz, texnik tafsilotlar bilan?

Logitlarning o'lchami qanday va ulardan qanday token olinadi?

Inferensning har bir bosqichida LLM chiqishda nima oladi?