mirror of
https://github.com/datawhalechina/llms-from-scratch-cn.git
synced 2026-05-01 11:58:17 +08:00
515 lines
22 KiB
Plaintext
515 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "f64be1c0-02a8-4ea9-ae05-85b66e803cac",
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"metadata": {},
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"outputs": [],
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"source": [
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"########################################################################################################\n",
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"# The RWKV Language Model - https://github.com/BlinkDL/RWKV-LM\n",
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"########################################################################################################\n",
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"\n",
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"import numpy as np\n",
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"np.set_printoptions(precision=4, suppress=True, linewidth=200)\n",
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"import types, torch\n",
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"import torch.nn as nn\n",
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"from torch.nn import functional as F\n",
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"\n",
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"MyModule = torch.jit.ScriptModule\n",
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"MyFunction = torch.jit.script_method"
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]
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},
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{
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"cell_type": "markdown",
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"id": "12f7fb18-23e7-44a1-883e-0bd673b8fb0b",
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"metadata": {},
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"source": [
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"\n",
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"\n",
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"图1：RWKV架构概述。左侧：时间混合和通道混合块；右上角：作为RNN单元的RWKV时间混合块；中下部：前馈模块中的令牌移位模块和Eagle时间混合；右下角：Finch时间混合中的令牌移位模块。所有形状注释为简洁起见假设为单头。虚线箭头（左侧，右上角）表示在Finch中有连接，但在Eagle中没有。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3d66b1f-5c04-44d7-9bbc-76f844707327",
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"metadata": {},
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"source": [
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"首先RWKV 6相比于RWKV 5在Token Shift上进行了改进，具体看下面的中间底部和右下角的图，分别是RWKV 4/5的Token Shift方式和RWKV 6的Token Shift方式。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "20cd8fa2-c1a4-4fdc-ba5d-858b25df1bcf",
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"metadata": {},
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"source": [
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"具体内容如下：\n",
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"\n",
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"### 公式部分\n",
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"\n",
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"Finch Token Shift中使用的数据依赖线性插值（ddlerp）定义如下：\n",
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"\n",
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"\\begin{align*}\n",
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"\\text{lora}_{\\Box}(x) &= \\lambda_{\\Box} + \\tanh(x A_{\\Box}) B_{\\Box} \\tag{14} \\\\\n",
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"\\text{ddlerp}_{\\Box}(a, b) &= a + (b - a) \\odot \\text{lora}_{\\Box}(a + (b - a) \\odot \\mu_{x}) \\tag{15}\n",
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"\\end{align*}\n",
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"\n",
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"### 解释部分\n",
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"\n",
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"- **可学习向量和矩阵**：\n",
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" - $\\mu_{x}$ 和每个 $\\lambda_{\\Box}$ 引入了维度为 $D$ 的可训练向量。\n",
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" - $A_{\\Box} \\in \\mathbb{R}^{D \\times 32}$ 和 $B_{\\Box} \\in \\mathbb{R}^{32 \\times D}$ 引入了新的可训练权重矩阵。\n",
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" - 对于公式中提到的LoRA$_{\\omega}$的特殊情况，引入了双倍大小的可训练权重矩阵：$A_{\\omega} \\in \\mathbb{R}^{D \\times 64}$ 和 $B_{\\omega} \\in \\mathbb{R}^{64 \\times D}$。\n",
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"\n",
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"- **未来模型扩展**：\n",
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" - 图1中右下角显示了一个示意图。\n",
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" - 未来7B及更大规模的Finch模型预计将进一步增加这些权重矩阵的大小（可能翻倍或更多）。\n",
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"\n",
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"### 功能与作用\n",
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"\n",
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"这种带有数据依赖性的Token Shift新形式旨在扩展模型超越RWKV-4/Eagle风格的Token Shift的能力，使得每个通道分配的新旧数据量现在依赖于当前和前一个时间步的输入。\n",
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"\n",
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"### 详细解释\n",
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"\n",
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"- **数据依赖线性插值（ddlerp）**：\n",
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" - ddlerp通过公式14和公式15实现，它结合了当前时间步和前一个时间步的信息来计算插值。\n",
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" - $\\text{lora}_{\\Box}(x)$利用了一个$\\lambda_{\\Box}$向量和通过$\\tanh$函数处理的$x A_{\\Box}$与$B_{\\Box}$的乘积来生成。\n",
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"\n",
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"- **模型能力扩展**：\n",
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" - 通过这种数据依赖的Token Shift，Finch模型能够更灵活地处理时间步之间的信息传递，使得模型在处理复杂序列数据时更加精确和高效。\n",
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"\n",
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"总结来说，Finch在Token Shift上引入了数据依赖的线性插值，利用可训练的向量和矩阵来增强模型的灵活性和能力，使其能够更好地处理时间步之间的信息，从而提高了模型的整体性能。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "1261d4e1-df4e-410b-a4fa-45452c4b6fb1",
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"metadata": {},
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"outputs": [],
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"source": [
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"class RWKV_TOKENIZER():\n",
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" table: list[list[list[bytes]]]\n",
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" good: list[set[int]]\n",
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" wlen: list[int]\n",
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" def __init__(self, file_name):\n",
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" self.idx2token = {}\n",
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" sorted = [] # must be already sorted\n",
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" lines = open(file_name, \"r\", encoding=\"utf-8\").readlines()\n",
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" for l in lines:\n",
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" idx = int(l[:l.index(' ')])\n",
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" x = eval(l[l.index(' '):l.rindex(' ')])\n",
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" x = x.encode(\"utf-8\") if isinstance(x, str) else x\n",
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" assert isinstance(x, bytes)\n",
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" assert len(x) == int(l[l.rindex(' '):])\n",
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" sorted += [x]\n",
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" self.idx2token[idx] = x\n",
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"\n",
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" self.token2idx = {}\n",
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" for k, v in self.idx2token.items():\n",
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" self.token2idx[v] = int(k)\n",
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"\n",
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" # precompute some tables for fast matching\n",
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" self.table = [[[] for j in range(256)] for i in range(256)]\n",
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" self.good = [set() for i in range(256)]\n",
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" self.wlen = [0 for i in range(256)]\n",
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"\n",
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" for i in reversed(range(len(sorted))): # reverse order - match longer tokens first\n",
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" s = sorted[i]\n",
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" if len(s) >= 2:\n",
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" s0 = int(s[0])\n",
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" s1 = int(s[1])\n",
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" self.table[s0][s1] += [s]\n",
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" self.wlen[s0] = max(self.wlen[s0], len(s))\n",
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" self.good[s0].add(s1)\n",
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"\n",
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" def encodeBytes(self, src: bytes) -> list[int]:\n",
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" src_len: int = len(src)\n",
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" tokens: list[int] = []\n",
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" i: int = 0\n",
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" while i < src_len:\n",
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" s: bytes = src[i : i + 1]\n",
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"\n",
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" if i < src_len - 1:\n",
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" s1: int = int(src[i + 1])\n",
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" s0: int = int(src[i])\n",
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" if s1 in self.good[s0]:\n",
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" sss: bytes = src[i : i + self.wlen[s0]]\n",
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" try:\n",
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" s = next(filter(sss.startswith, self.table[s0][s1]))\n",
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" except:\n",
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" pass\n",
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" tokens.append(self.token2idx[s])\n",
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" i += len(s)\n",
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"\n",
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" return tokens\n",
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"\n",
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" def decodeBytes(self, tokens):\n",
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" return b''.join(map(lambda i: self.idx2token[i], tokens))\n",
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"\n",
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" def encode(self, src: str):\n",
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" return self.encodeBytes(src.encode(\"utf-8\"))\n",
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"\n",
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" def decode(self, tokens):\n",
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" return self.decodeBytes(tokens).decode('utf-8')\n",
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"\n",
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" def printTokens(self, tokens):\n",
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" for i in tokens:\n",
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" s = self.idx2token[i]\n",
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" try:\n",
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" s = s.decode('utf-8')\n",
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" except:\n",
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" pass\n",
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" print(f'{repr(s)}{i}', end=' ')\n",
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" # print(repr(s), i)\n",
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" print()\n",
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"\n",
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"########################################################################################################"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "725bc55e-7f3f-4c1c-9664-ad84bf68e943",
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"metadata": {},
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"outputs": [],
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"source": [
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"#采样方式没有变化\n",
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"def sample_logits(out, temperature=1.0, top_p=0.8):\n",
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" probs = F.softmax(out, dim=-1).numpy()\n",
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" sorted_probs = np.sort(probs)[::-1]\n",
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" cumulative_probs = np.cumsum(sorted_probs)\n",
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" cutoff = float(sorted_probs[np.argmax(cumulative_probs > top_p)])\n",
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" probs[probs < cutoff] = 0\n",
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" if temperature != 1.0:\n",
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" probs = probs.pow(1.0 / temperature)\n",
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" probs = probs / np.sum(probs)\n",
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" out = np.random.choice(a=len(probs), p=probs)\n",
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" return out\n",
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"\n",
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"########################################################################################################"
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]
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},
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{
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"cell_type": "raw",
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"id": "812fac97-a6b8-423c-831d-fe7397883437",
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"metadata": {},
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"source": [
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"模型下载地址：https://hf-mirror.com/BlinkDL/rwkv-6-world/resolve/main/RWKV-x060-World-1B6-v2.1-20240328-ctx4096.pth"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "434ff88e-b94e-4f8b-86a3-7fefca32cddb",
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenizer = RWKV_TOKENIZER(\"./rwkv_vocab_v20230424.txt\")\n",
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"\n",
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"args = types.SimpleNamespace()\n",
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"args.MODEL_NAME = '/data1/ckw/RWKV-x060-World-1B6-v2.1-20240328-ctx4096'\n",
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"args.n_layer = 24\n",
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"args.n_embd = 2048\n",
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"args.vocab_size = 65536\n",
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"\n",
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"context = \"\\nDatawhale is \"\n",
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"# context = \"\\n我们发现\"\n",
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"NUM_TRIALS = 3\n",
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"LENGTH_PER_TRIAL = 100\n",
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"TEMPERATURE = 1.0\n",
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"TOP_P = 0.7"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ee395717-e599-40fd-a4b9-ddfc800babea",
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"metadata": {},
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"source": [
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"相比于RWKV 5的Channel Mixing（见下面代码）来说，RWKV6的Channel Mixing没有变化，这里的`time_maa_k`和RWKV 5中的`time_mix_k`是相同形状的可学习参数，都是一个维度为D（模型的隐藏层维度）的张量。"
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]
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},
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{
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"cell_type": "markdown",
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"id": "54571fa3-52c6-4b05-ab97-9f06d76a522e",
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"metadata": {},
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"source": [
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"Finch在时间混合（Time Mixing）上做了以下改进，具体内容如下：\n",
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"\n",
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"### 公式部分\n",
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"\n",
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"Finch时间混合的公式如下：\n",
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"\n",
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"\\begin{align*}\n",
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"\\Box_t &= \\text{lerp}_{\\Box}(x_t, x_{t-1}) W_{\\Box}, \\quad \\Box \\in \\{r, k, v, g\\} \\tag{16} \\\\\n",
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"d_t &= \\text{lora}_d(\\text{ddlerp}_d(x_t, x_{t-1})) \\tag{17} \\\\\n",
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"w_t &= \\exp(-\\exp(d_t)) \\tag{18} \\\\\n",
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"\\text{wk} \\mathbf{v}_t &= \\text{diag}(u) \\cdot k_t^\\top \\cdot v_t + \\sum_{i=1}^{t-1} \\left( \\prod_{j=1}^{i-1} w_j \\right) \\cdot k_i^\\top \\cdot v_i \\in \\mathbb{R}^{(D/h) \\times (D/h)} \\tag{19} \\\\\n",
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"o_t &= \\text{concat} \\left( \\text{SiLU}(g_t) \\odot \\text{LayerNorm}(r_t \\cdot \\text{wk} \\mathbf{v}_t) \\right) W_o \\in \\mathbb{R}^D \\tag{20}\n",
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"\\end{align*}\n",
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"\n",
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"### 解释部分\n",
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"\n",
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"- **可学习向量和矩阵**：\n",
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" - $\\Box_t$ 是通过线性插值（lerp）计算得到的，适用于接受度（receptance）、键（key）、值（value）和门控向量（gate vectors）。\n",
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" - $d_t$ 是通过 $\\text{lora}_d$ 函数对 $\\text{ddlerp}_d(x_t, x_{t-1})$ 进行处理得到的。\n",
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" - $w_t$ 是由 $d_t$ 计算得到的，用于控制衰减的动态变化。\n",
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"\n",
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"- **时间混合计算**：\n",
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" - $\\text{wk} \\mathbf{v}_t$ 是通过当前键值对 $k_t^\\top \\cdot v_t$ 和所有之前时间步的键值对 $k_i^\\top \\cdot v_i$ 的加权和计算得到的，权重由 $w_t$ 控制。\n",
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" - 输出 $o_t$ 是通过连接（concat） $\\text{SiLU}(g_t)$ 和 $\\text{LayerNorm}(r_t \\cdot \\text{wk} \\mathbf{v}_t)$ 的结果得到的。\n",
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"\n",
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"- **递归形式**：\n",
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" \\begin{align*}\n",
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" \\text{wk} \\mathbf{v}' &= s + \\text{diag}(u) \\cdot k^\\top \\cdot v \\tag{21} \\\\\n",
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" s' &= \\text{diag}(w) \\cdot s + k^\\top \\cdot v \\tag{22}\n",
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" \\end{align*}\n",
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"\n",
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"### 功能与作用\n",
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"\n",
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"与Eagle不同，Finch中的 $w_t$ 不是在整个序列中固定的。每个通道的 $w_t$ 可以随时间动态变化，具体取决于数据输入，这也是Finch中衰减的核心变化。\n",
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"\n",
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"### 详细解释\n",
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"\n",
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"- **动态衰减**：\n",
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" - Finch引入的数据依赖衰减使得每个通道的 $w_t$ 可以根据当前和之前的输入动态变化，而不是固定的学习向量。\n",
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" - 这种动态衰减机制通过新的LoRA机制应用到学习向量上，增加了模型的灵活性。\n",
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"\n",
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"- **高级Token-Shift**：\n",
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" - 新的时间衰减 $w_t$ 进一步应用了LoRA机制，允许每个通道的 $w_t$ 基于当前和之前的令牌混合来变化。\n",
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"\n",
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"总结来说，Finch在时间混合上通过引入数据依赖的动态衰减机制和高级Token-Shift，实现了更高的灵活性和精确度，使模型能够更好地处理和融合时间步之间的信息，从而提高了整体性能。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "3869854a-a4e3-4652-9698-b0d81bbbd645",
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"metadata": {},
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"outputs": [],
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"source": [
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"class RWKV_RNN(MyModule):\n",
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" def __init__(self, args):\n",
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" super().__init__()\n",
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" self.args = args\n",
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" self.eval() # set torch to inference mode\n",
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" \n",
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" w = torch.load(args.MODEL_NAME + '.pth', map_location='cpu')\n",
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"\n",
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" for k in w.keys():\n",
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" w[k] = w[k].float() # convert to f32 type\n",
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" if '.time_' in k: w[k] = w[k].squeeze()\n",
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" if '.time_faaaa' in k: w[k] = w[k].unsqueeze(-1)\n",
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"\n",
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" self.n_head = w['blocks.0.att.time_faaaa'].shape[0]\n",
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" self.head_size = w['blocks.0.ln1.weight'].shape[0] // self.n_head\n",
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" \n",
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" self.w = types.SimpleNamespace() # set self.w from w\n",
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" self.w.blocks = {}\n",
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" for k in w.keys(): # example: \"blocks.0.att.time_first\" => self.w.blocks[0].att.time_first\n",
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" parts = k.split('.')\n",
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" last = parts.pop()\n",
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" here = self.w\n",
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" for p in parts:\n",
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" if p.isdigit():\n",
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" p = int(p)\n",
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" if p not in here: here[p] = types.SimpleNamespace()\n",
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" here = here[p]\n",
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" else:\n",
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" if not hasattr(here, p): setattr(here, p, types.SimpleNamespace())\n",
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" here = getattr(here, p)\n",
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" setattr(here, last, w[k])\n",
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"\n",
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" def layer_norm(self, x, w):\n",
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" return F.layer_norm(x, (self.args.n_embd,), weight=w.weight, bias=w.bias)\n",
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"\n",
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" @MyFunction\n",
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" def channel_mixing(self, x, state, i:int, time_maa_k, time_maa_r, kw, vw, rw):\n",
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" i0 = (2+self.head_size)*i+0\n",
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" sx = state[i0] - x\n",
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" xk = x + sx * time_maa_k\n",
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" xr = x + sx * time_maa_r\n",
|
||
" state[i0] = x\n",
|
||
" r = torch.sigmoid(rw @ xr)\n",
|
||
" k = torch.square(torch.relu(kw @ xk)) # square relu, primer paper\n",
|
||
" return r * (vw @ k)\n",
|
||
"\n",
|
||
" @MyFunction\n",
|
||
" def time_mixing(self, x, state, i:int, x_maa, w_maa, k_maa, v_maa, r_maa, g_maa, tm_w1, tm_w2, td_w1, td_w2, time_first, time_decay, kw, vw, rw, gw, ow, ln_w, ln_b):\n",
|
||
" H = self.n_head\n",
|
||
" S = self.head_size\n",
|
||
"\n",
|
||
" i1 = (2+S)*i+1\n",
|
||
" sx = state[i1] - x\n",
|
||
" state[i1] = x\n",
|
||
" xxx = x + sx * x_maa\n",
|
||
" xxx = torch.tanh(xxx @ tm_w1).view(5, 1, -1)\n",
|
||
" xxx = torch.bmm(xxx, tm_w2).view(5, -1)\n",
|
||
" mw, mk, mv, mr, mg = xxx.unbind(dim=0)\n",
|
||
"\n",
|
||
" xw = x + sx * (w_maa + mw)\n",
|
||
" xk = x + sx * (k_maa + mk)\n",
|
||
" xv = x + sx * (v_maa + mv)\n",
|
||
" xr = x + sx * (r_maa + mr)\n",
|
||
" xg = x + sx * (g_maa + mg)\n",
|
||
"\n",
|
||
" w = (time_decay + (torch.tanh(xw @ td_w1) @ td_w2).float()).view(H, S, 1)\n",
|
||
" w = torch.exp(-torch.exp(w.float()))\n",
|
||
"\n",
|
||
" r = (rw @ xr).view(H, 1, S)\n",
|
||
" k = (kw @ xk).view(H, S, 1)\n",
|
||
" v = (vw @ xv).view(H, 1, S)\n",
|
||
" g = F.silu(gw @ xg)\n",
|
||
"\n",
|
||
" s = state[(2+S)*i+2:(2+S)*(i+1), :].reshape(H, S, S)\n",
|
||
"\n",
|
||
" x = torch.zeros(H, S)\n",
|
||
" a = k @ v\n",
|
||
" x = r @ (time_first * a + s)\n",
|
||
" s = a + w * s\n",
|
||
" \n",
|
||
" state[(2+S)*i+2:(2+S)*(i+1), :] = s.reshape(S, -1)\n",
|
||
" x = x.flatten()\n",
|
||
"\n",
|
||
" x = F.group_norm(x.unsqueeze(0), num_groups=H, weight=ln_w, bias=ln_b, eps = 64e-5).squeeze(0) * g # same as gn(x/8, eps=1e-5)\n",
|
||
" return ow @ x\n",
|
||
"\n",
|
||
" def forward(self, token, state):\n",
|
||
" with torch.no_grad():\n",
|
||
" if state == None:\n",
|
||
" state = torch.zeros(self.args.n_layer * (2+self.head_size), self.args.n_embd)\n",
|
||
" \n",
|
||
" x = self.w.emb.weight[token]\n",
|
||
" x = self.layer_norm(x, self.w.blocks[0].ln0)\n",
|
||
" for i in range(self.args.n_layer):\n",
|
||
" att = self.w.blocks[i].att\n",
|
||
" x = x + self.time_mixing(self.layer_norm(x, self.w.blocks[i].ln1), state, i,\n",
|
||
" att.time_maa_x, att.time_maa_w, att.time_maa_k, att.time_maa_v, att.time_maa_r, att.time_maa_g, att.time_maa_w1, att.time_maa_w2,\n",
|
||
" att.time_decay_w1, att.time_decay_w2, att.time_faaaa, att.time_decay,\n",
|
||
" att.key.weight, att.value.weight, att.receptance.weight, att.gate.weight, att.output.weight,\n",
|
||
" att.ln_x.weight, att.ln_x.bias)\n",
|
||
" ffn = self.w.blocks[i].ffn\n",
|
||
" x = x + self.channel_mixing(self.layer_norm(x, self.w.blocks[i].ln2), state, i, \n",
|
||
" ffn.time_maa_k, ffn.time_maa_r, \n",
|
||
" ffn.key.weight, ffn.value.weight, ffn.receptance.weight)\n",
|
||
" \n",
|
||
" x = self.w.head.weight @ self.layer_norm(x, self.w.ln_out)\n",
|
||
" return x.float(), state"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "5235be83-a574-41f6-8546-bc415e2aeacf",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Using CPU. Loading /data1/ckw/RWKV-x060-World-1B6-v2.1-20240328-ctx4096 ...\n",
|
||
"\n",
|
||
"Preprocessing context (slow version. see v2/rwkv/model.py for fast version)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f'\\nUsing CPU. Loading {args.MODEL_NAME} ...')\n",
|
||
"model = RWKV_RNN(args)\n",
|
||
"\n",
|
||
"print(f'\\nPreprocessing context (slow version. see v2/rwkv/model.py for fast version)')\n",
|
||
"init_state = None"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "035ce374-c1c0-43b8-9ba9-37df297baae6",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"for token in tokenizer.encode(context):\n",
|
||
" init_out, init_state = model.forward(token, init_state)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "1e17d6ef-c02c-4d27-8cf6-9a262f75f77f",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"\n",
|
||
"--[ Trial 0 ]----------------- \n",
|
||
"Datawhale is ➡️‼️\n",
|
||
"https://twitter.com/datawhale_cn/status/1463997087819689985\n",
|
||
"#Data #AI #DataAnalytics #AIOps #DataOps #MachineLearning #DataScience #DataLakeAnalytics #Hadoop #Amazon #Google #AWS #Azure #Dataprep #DevOps #OSS #Linux #Unix #BigData #BigDataOps #DataArchitecture #DataScienceOps #MachineLearningOps\n",
|
||
"\n",
|
||
"--[ Trial 1 ]----------------- \n",
|
||
"Datawhale is 🤓\n",
|
||
"\n",
|
||
"--[ Trial 2 ]----------------- \n",
|
||
"Datawhale is 🤯. They have a solid team, a really good SaaS product and the tools to support their users. That said, I have to take a serious look at the privacy and security of their platform before I buy into their story. I think this is a case of big companies buying into the hype, and they're not taking into account all the realities that go into building a privacy-focused product.\n",
|
||
"P.S. You can still apply to Datawhale's Program.\n",
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"for TRIAL in range(NUM_TRIALS):\n",
|
||
" print(f'\\n\\n--[ Trial {TRIAL} ]-----------------', context, end=\"\")\n",
|
||
" all_tokens = []\n",
|
||
" out_last = 0\n",
|
||
" out, state = init_out.clone(), init_state.clone()\n",
|
||
" for i in range(LENGTH_PER_TRIAL):\n",
|
||
" token = sample_logits(out, TEMPERATURE, TOP_P)\n",
|
||
" all_tokens += [token]\n",
|
||
" try:\n",
|
||
" tmp = tokenizer.decode(all_tokens[out_last:])\n",
|
||
" if '\\ufffd' not in tmp: # only print when we have a valid utf-8 string\n",
|
||
" print(tmp, end=\"\", flush=True)\n",
|
||
" out_last = i + 1\n",
|
||
" except:\n",
|
||
" pass\n",
|
||
" out, state = model.forward(token, state) \n",
|
||
"print('\\n')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "172c33e0-6d5b-4143-b85d-86777a2f5739",
|
||
"metadata": {},
|
||
"source": [
|
||
"v6和v5相比，感觉更喜欢使用emoj了哈哈"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "kewei-ai",
|
||
"language": "python",
|
||
"name": "kewei-ai"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.11.5"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|