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Jira 标记速查

标题

h1. ～ h6. 分别对应一级至六级标题。

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#!/usr/bin/env fish

function check_var -a var msg
    if test -z $var; echo $msg >&2; exit 1; end
end

function check_status -a msg
    if test $status != 0; echo $msg >&2; exit 1; end
end

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(async () => {
    const Main = await import('resource:///org/gnome/shell/ui/main.js');
    const { default: GLib } = await import('gi://GLib');

    const ind = Main.panel.statusArea.keyboard;
    const mgr = ind?._inputSourceManager;
    if (!mgr) return;

    const update = () => {
        const text = ind._indicatorLabels[mgr.currentSource?.index]?.get_text();

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/* gcc -O2 -march=native -o kgp kgp.c -lm */
#define _POSIX_C_SOURCE 200809L

#include <math.h>
#include <signal.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>

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# pip install torch huggingface_hub safetensors tokenizers transformers
import sys, re, time
from pathlib import Path
from collections import defaultdict

import torch
import torch.nn as nn
import torch.nn.functional as F
from tokenizers import Tokenizer
from safetensors import safe_open

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#include <poll.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <termios.h>
#include <unistd.h>

struct termios orig_termios;

void reset_terminal() {

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from struct import pack, unpack_from


class MKFDecoder(object):
    def __init__(self, path=None):
        assert path or data
        with open(path, "rb") as f:
            self._content = memoryview(f.read())
        self.count = unpack_from("<I", self._content, 0)[0] // 4
        self.indexes = tuple(

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from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.functional as F
from safetensors import safe_open
from tokenizers import Tokenizer

CFG = {
    "vocab_size": 262_144,
    "context_length": 32_768,

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from dataclasses import dataclass
from pathlib import Path

import torch
from torch import nn
import torch.nn.functional as F

from safetensors import safe_open
from tokenizers import Tokenizer

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import os
from functools import lru_cache

import torch
from torch import nn
import torch.nn.functional as F

from transformers import Qwen3Config
from transformers import Qwen2TokenizerFast