{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "machine_shape": "hm", "gpuType": "A100" }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU" }, "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "G6BvseJ-0VwS", "outputId": "72cafdf8-dd7b-4412-a9bc-2cfcebfb6949" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "--2023-11-03 11:10:34-- https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt\n", "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n", "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 1115394 (1.1M) [text/plain]\n", "Saving to: ‘input.txt’\n", "\n", "input.txt 100%[===================>] 1.06M --.-KB/s in 0.02s \n", "\n", "2023-11-03 11:10:35 (48.3 MB/s) - ‘input.txt’ saved [1115394/1115394]\n", "\n" ] } ], "source": [ "!wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt" ] }, { "cell_type": "code", "source": [ "with open('input.txt', 'r', encoding='utf-8') as f:\n", " text = f.read()" ], "metadata": { "id": "pxZym4QU1mCq" }, "execution_count": 11, "outputs": [] }, { "cell_type": "code", "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.nn import functional as F\n", "\n", "# hyperparameters\n", "batch_size = 16 # how many independent sequences will we process in parallel?\n", "block_size = 32 # what is the maximum context length for predictions?\n", "max_iters = 5000\n", "eval_interval = 100\n", "learning_rate = 1e-3\n", "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n", "eval_iters = 200\n", "n_embd = 64\n", "n_head = 4\n", "n_layer = 4\n", "dropout = 0.0\n", "\n", "torch.manual_seed(1337)\n", "\n", "\n", "# here are all the unique characters that occur in this text\n", "chars = sorted(list(set(text)))\n", "vocab_size = len(chars)\n", "# create a mapping from characters to integers\n", "stoi = { ch:i for i,ch in enumerate(chars) }\n", "itos = { i:ch for i,ch in enumerate(chars) }\n", "encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers\n", "decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string\n", "\n", "# Train and test splits\n", "data = torch.tensor(encode(text), dtype=torch.long)\n", "n = int(0.9*len(data)) # first 90% will be train, rest val\n", "train_data = data[:n]\n", "val_data = data[n:]\n", "\n", "# data loading\n", "def get_batch(split):\n", " # generate a small batch of data of inputs x and targets y\n", " data = train_data if split == 'train' else val_data\n", " ix = torch.randint(len(data) - block_size, (batch_size,))\n", " x = torch.stack([data[i:i+block_size] for i in ix])\n", " y = torch.stack([data[i+1:i+block_size+1] for i in ix])\n", " x, y = x.to(device), y.to(device)\n", " return x, y\n", "\n", "@torch.no_grad()\n", "def estimate_loss():\n", " out = {}\n", " model.eval()\n", " for split in ['train', 'val']:\n", " losses = torch.zeros(eval_iters)\n", " for k in range(eval_iters):\n", " X, Y = get_batch(split)\n", " logits, loss = model(X, Y)\n", " losses[k] = loss.item()\n", " out[split] = losses.mean()\n", " model.train()\n", " return out\n", "\n", "class Head(nn.Module):\n", " \"\"\" one head of self-attention \"\"\"\n", "\n", " def __init__(self, head_size):\n", " super().__init__()\n", " self.key = nn.Linear(n_embd, head_size, bias=False)\n", " self.query = nn.Linear(n_embd, head_size, bias=False)\n", " self.value = nn.Linear(n_embd, head_size, bias=False)\n", " self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))\n", "\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " def forward(self, x):\n", " B,T,C = x.shape\n", " k = self.key(x) # (B,T,C)\n", " q = self.query(x) # (B,T,C)\n", " # compute attention scores (\"affinities\")\n", " wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, T) -> (B, T, T)\n", " wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)\n", " wei = F.softmax(wei, dim=-1) # (B, T, T)\n", " wei = self.dropout(wei)\n", " # perform the weighted aggregation of the values\n", " v = self.value(x) # (B,T,C)\n", " out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)\n", " return out\n", "\n", "class MultiHeadAttention(nn.Module):\n", " \"\"\" multiple heads of self-attention in parallel \"\"\"\n", "\n", " def __init__(self, num_heads, head_size):\n", " super().__init__()\n", " self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])\n", " self.proj = nn.Linear(n_embd, n_embd)\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " def forward(self, x):\n", " out = torch.cat([h(x) for h in self.heads], dim=-1)\n", " out = self.dropout(self.proj(out))\n", " return out\n", "\n", "class FeedFoward(nn.Module):\n", " \"\"\" a simple linear layer followed by a non-linearity \"\"\"\n", "\n", " def __init__(self, n_embd):\n", " super().__init__()\n", " self.net = nn.Sequential(\n", " nn.Linear(n_embd, 4 * n_embd),\n", " nn.ReLU(),\n", " nn.Linear(4 * n_embd, n_embd),\n", " nn.Dropout(dropout),\n", " )\n", "\n", " def forward(self, x):\n", " return self.net(x)\n", "\n", "class Block(nn.Module):\n", " \"\"\" Transformer block: communication followed by computation \"\"\"\n", "\n", " def __init__(self, n_embd, n_head):\n", " # n_embd: embedding dimension, n_head: the number of heads we'd like\n", " super().__init__()\n", " head_size = n_embd // n_head\n", " self.sa = MultiHeadAttention(n_head, head_size)\n", " self.ffwd = FeedFoward(n_embd)\n", " self.ln1 = nn.LayerNorm(n_embd)\n", " self.ln2 = nn.LayerNorm(n_embd)\n", "\n", " def forward(self, x):\n", " x = x + self.sa(self.ln1(x))\n", " x = x + self.ffwd(self.ln2(x))\n", " return x\n", "\n", "# super simple bigram model\n", "class BigramLanguageModel(nn.Module):\n", "\n", " def __init__(self):\n", " super().__init__()\n", " # each token directly reads off the logits for the next token from a lookup table\n", " self.token_embedding_table = nn.Embedding(vocab_size, n_embd)\n", " self.position_embedding_table = nn.Embedding(block_size, n_embd)\n", " self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])\n", " self.ln_f = nn.LayerNorm(n_embd) # final layer norm\n", " self.lm_head = nn.Linear(n_embd, vocab_size)\n", "\n", " def forward(self, idx, targets=None):\n", " B, T = idx.shape\n", "\n", " # idx and targets are both (B,T) tensor of integers\n", " tok_emb = self.token_embedding_table(idx) # (B,T,C)\n", " pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)\n", " x = tok_emb + pos_emb # (B,T,C)\n", " x = self.blocks(x) # (B,T,C)\n", " x = self.ln_f(x) # (B,T,C)\n", " logits = self.lm_head(x) # (B,T,vocab_size)\n", "\n", " if targets is None:\n", " loss = None\n", " else:\n", " B, T, C = logits.shape\n", " logits = logits.view(B*T, C)\n", " targets = targets.view(B*T)\n", " loss = F.cross_entropy(logits, targets)\n", "\n", " return logits, loss\n", "\n", " def generate(self, idx, max_new_tokens):\n", " # idx is (B, T) array of indices in the current context\n", " for _ in range(max_new_tokens):\n", " # crop idx to the last block_size tokens\n", " idx_cond = idx[:, -block_size:]\n", " # get the predictions\n", " logits, loss = self(idx_cond)\n", " # focus only on the last time step\n", " logits = logits[:, -1, :] # becomes (B, C)\n", " # apply softmax to get probabilities\n", " probs = F.softmax(logits, dim=-1) # (B, C)\n", " # sample from the distribution\n", " idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)\n", " # append sampled index to the running sequence\n", " idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)\n", " return idx\n", "\n", "model = BigramLanguageModel()\n", "m = model.to(device)\n", "# print the number of parameters in the model\n", "print(sum(p.numel() for p in m.parameters())/1e6, 'M parameters')\n", "\n", "# create a PyTorch optimizer\n", "optimizer = torch.optim.AdamW(model.parameters(), lr=learning_rate)\n", "\n", "for iter in range(max_iters):\n", "\n", " # every once in a while evaluate the loss on train and val sets\n", " if iter % eval_interval == 0 or iter == max_iters - 1:\n", " losses = estimate_loss()\n", " print(f\"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}\")\n", "\n", " # sample a batch of data\n", " xb, yb = get_batch('train')\n", "\n", " # evaluate the loss\n", " logits, loss = model(xb, yb)\n", " optimizer.zero_grad(set_to_none=True)\n", " loss.backward()\n", " optimizer.step()\n", "\n", "# generate from the model\n", "context = torch.zeros((1, 1), dtype=torch.long, device=device)\n", "print(decode(m.generate(context, max_new_tokens=2000)[0].tolist()))\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "U_mrE9Vd10Ab", "outputId": "f443c391-fd7f-4c2d-dd0d-72ef25849ef6" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "0.209729 M parameters\n", "step 0: train loss 4.4116, val loss 4.4022\n", "step 100: train loss 2.6568, val loss 2.6670\n", "step 200: train loss 2.5091, val loss 2.5060\n", "step 300: train loss 2.4199, val loss 2.4337\n", "step 400: train loss 2.3500, val loss 2.3563\n", "step 500: train loss 2.2961, val loss 2.3126\n", "step 600: train loss 2.2408, val loss 2.2501\n", "step 700: train loss 2.2053, val loss 2.2187\n", "step 800: train loss 2.1636, val loss 2.1870\n", "step 900: train loss 2.1226, val loss 2.1483\n", "step 1000: train loss 2.1017, val loss 2.1283\n", "step 1100: train loss 2.0683, val loss 2.1174\n", "step 1200: train loss 2.0376, val loss 2.0798\n", "step 1300: train loss 2.0256, val loss 2.0645\n", "step 1400: train loss 1.9919, val loss 2.0362\n", "step 1500: train loss 1.9696, val loss 2.0304\n", "step 1600: train loss 1.9625, val loss 2.0470\n", "step 1700: train loss 1.9402, val loss 2.0119\n", "step 1800: train loss 1.9085, val loss 1.9957\n", "step 1900: train loss 1.9080, val loss 1.9869\n", "step 2000: train loss 1.8834, val loss 1.9941\n", "step 2100: train loss 1.8727, val loss 1.9758\n", "step 2200: train loss 1.8585, val loss 1.9622\n", "step 2300: train loss 1.8537, val loss 1.9503\n", "step 2400: train loss 1.8419, val loss 1.9424\n", "step 2500: train loss 1.8153, val loss 1.9407\n", "step 2600: train loss 1.8267, val loss 1.9374\n", "step 2700: train loss 1.8126, val loss 1.9344\n", "step 2800: train loss 1.8054, val loss 1.9230\n", "step 2900: train loss 1.8045, val loss 1.9339\n", "step 3000: train loss 1.7963, val loss 1.9243\n", "step 3100: train loss 1.7691, val loss 1.9208\n", "step 3200: train loss 1.7506, val loss 1.9092\n", "step 3300: train loss 1.7548, val loss 1.9038\n", "step 3400: train loss 1.7582, val loss 1.8960\n", "step 3500: train loss 1.7376, val loss 1.8934\n", "step 3600: train loss 1.7232, val loss 1.8888\n", "step 3700: train loss 1.7280, val loss 1.8814\n", "step 3800: train loss 1.7221, val loss 1.8951\n", "step 3900: train loss 1.7228, val loss 1.8789\n", "step 4000: train loss 1.7168, val loss 1.8635\n", "step 4100: train loss 1.7168, val loss 1.8798\n", "step 4200: train loss 1.7088, val loss 1.8672\n", "step 4300: train loss 1.6995, val loss 1.8501\n", "step 4400: train loss 1.7096, val loss 1.8686\n", "step 4500: train loss 1.6907, val loss 1.8546\n", "step 4600: train loss 1.6868, val loss 1.8348\n", "step 4700: train loss 1.6786, val loss 1.8346\n", "step 4800: train loss 1.6659, val loss 1.8445\n", "step 4900: train loss 1.6711, val loss 1.8384\n", "step 4999: train loss 1.6630, val loss 1.8230\n", "\n", "ROMEO:\n", "But you far you\n", "my swap with thus; come hath I uD\n", "If sleemition of where's granded\n", "Of their of tout the gortune upwon alond, liege man to is Iell this surpe\n", "And than sleue thus mind, his by blow,\n", "Virdty toward butied, Ditire spresiss with thou some not.\n", "\n", "LORIO:\n", "I am part\n", "But thou sging them but\n", "shat secondes morry thou sovore.\n", "\n", "ISABUS:\n", "What art sade but hither, thange e'en,\n", "Protes as kingle me; an your tords whom are Ineal.\n", "\n", "MENENIUS:\n", "But little sweet, hom, foust cerfort;\n", "Winth hing diend enirs' tompy beds sick ways!\n", "What curforself this grace. Won, passes us.\n", "\n", "BUCKINGHABY MARD:\n", "Mether star: keep it any head which\n", "He tall devioly that, out that confer old.\n", "Our thy dears time.\n", "Nay, the fragoly, pair, of new\n", "my father, my lip Backnoward:\n", "God therring for respide\n", "What colvery, teminelyord, I mast,\n", "While us that such differs I'll that confect I come,\n", "But; man.\n", "\n", "VOLUMNIO:\n", "Ontread confail with me. Humser dipporbried answeraw is codal one,\n", "Onjestion, not or cheavess ensty with.\n", "\n", "GLOUCESTER:\n", "\n", "HENRY Mess to Lies?\n", "Stand and these beguare youf stile that than war\n", "offity are, I usquesch\n", "Frown movhapty not duke with you addom\n", "grack prowd--lost\n", "But but they worse is senst my crunne undolier. But, beauts pruntaly; I stoll'ct my nor Murder, I sot, though who speak\n", "Your bout told-man rathing if anyshal\n", "epitence, tirre no the said he's,\n", "Andis frultifs. what his lide? That mirdy this dudgetions?\n", "\n", "KING ARINIA:\n", "I let holt not sucKether,\n", "Whither, efore But lord: I, beget because at that his say\n", "as to brought grave a donesmer all nobe.\n", "\n", "BUCKINGHUMBY:\n", "Which forgeled! Came; nor thereforn's fiends strefet.\n", "\n", "PLORIA:\n", "Yet to Capprohning, that brird\n", "of say mover a desrick.\n", "\n", "MO\n", "stompars, God the\n", "citchard is high.\n", "\n", "Seth Second Methere:\n", "Marrmat I unmale the bretcius unfoect that I would back where own thy lurges\n", "And, iffillimorture:\n", "As thou twand, York these that high praut.\n", "Plafe merprates sure dread with her,\n", "At not your must I suchon? too prant!\n", "O 'hiles clight the bleave is graved before\n" ] } ] }, { "cell_type": "code", "source": [ "# generate from the model\n", "context = torch.zeros((1, 1), dtype=torch.long, device=device)\n", "print(decode(m.generate(context, max_new_tokens=2000)[0].tolist()))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M0qIA2GK2qzI", "outputId": "86126a68-17b1-4171-920a-1d2df6fa3f1a" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "And thou to lesserve his his know'st broy by A towe than or fuch dight none worthy'st countinne, congess\n", "our ire Iname's marriatate the entrity?\n", "\n", "COMIOLUS:\n", "Yet there me your let thy by courtary, own but I cannot, to\n", "you.\n", "\n", "MOth Osque, while and nett; pity, brow umput;\n", "He betwered's prettedy if not you arter,\n", "But woman furner his good me to ambled thy follows\n", "Gents for you daying this distend and he but.\n", "\n", "COMINANUS:\n", "But you know wish the wear? whoe not to breave maste gate?\n", "Not, now you read own. Lo-honour shoes\n", "honordore vilibert.\n", "\n", "ARTOS:\n", "Nay, as Is theen, God\n", "Were I saying cose\n", "Will there's upon and tools.\n", "\n", "HORSIO:\n", "Pomfort life?\n", "Whereform make comps hersed, my what away,\n", "Go'st Your haste entens, and succe?\n", "\n", "LORD RIARENCE:\n", "Fies my like, wifch a my nobt.\n", "And!\n", "And ways. Whithing death.\n", "\n", "CORIOLUMNO:\n", "It must I have grawits.-\n", "Ris Gomisty yor then thin dot this no all-donged,\n", "But quarry the latter: Have me the betime twooke steed to blood\n", "That his rysour grower-foldds: bnot Plond,\n", "By that all wittore old the malt our liight.\n", "Would for not\n", "And sabet I sout ofing more in must.\n", "\n", "MENENIUS:\n", "Gor low your I standed\n", "To heavy:\n", "While to caid your inswoes!\n", "Thrhing the princlusior lurmeng,\n", "To Whie! entred mean the not, sare.\n", "\n", "BRUTUS:\n", "Is my partend him, if Is verys be,\n", "Whim you longs,\n", "Say, his me. Murselets; not with is most.\n", "\n", "JOLINA:\n", "That it where that thluse too the hath'd\n", "unsomed of our heavis'd?\n", "\n", "So his were Clamind:\n", "Ounly mistry's soul\n", "To once myser flow\n", "Which, then, whet must I as not drums as the ouch are\n", "burnse contreased and in Comintity?\n", "\n", "Mistray is I curliented:\n", "Thou herew bottust, How lad you blist a wear's art?\n", "What the vave--batta thing with\n", "that his my urtusaed and as mine, thus not,\n", "May your pohed me mhalt livy very\n", "But I sham I ham kitse, pean, for\n", "was, ewith woll heave in thou art, dlignt,\n", "Of fair Griward that remottes must;\n", "Cadyfore the not lords not, I say's gener which of your rame? Istand my hearth\n", "And thou alt nenget that shame\n", "She with them kinderire it put this\n" ] } ] }, { "cell_type": "code", "source": [ "prompt = \"Once upon a time\"\n", "context = torch.tensor(encode(prompt), dtype=torch.long, device=device).view(1, -1)\n", "print(decode(m.generate(context, max_new_tokens=200)[0].tolist()))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Na0SThjv5-iz", "outputId": "c649c8ad-42fe-4a77-a219-9dcb1857a9c0" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Once upon a times peacts mother saclaves is 'Then of my tonguen,\n", "Thus are been\n", "My my behot prilatte, what you brot,\n", "Speeke there is my bud the be, 'smandion from me:\n", "And the barttes, rechard, where capuse,\n", "Rentent, I\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "# Save the model\n", "torch.save(m.state_dict(), 'GPT_Shakespeare_language_model.pth')" ], "metadata": { "id": "sfmRYo9h6B24" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "# Load the model\n", "loaded_model = BigramLanguageModel() # Initialize an instance of your model\n", "loaded_model.load_state_dict(torch.load('GPT_Shakespeare_language_model.pth'))\n", "loaded_model.to(device).eval() # Set the model to evaluation mode" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "xO9JefxH6KHS", "outputId": "d7f0191c-4e02-4ed7-ff4b-b6f25a538fe5" }, "execution_count": 17, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "BigramLanguageModel(\n", " (token_embedding_table): Embedding(65, 64)\n", " (position_embedding_table): Embedding(32, 64)\n", " (blocks): Sequential(\n", " (0): Block(\n", " (sa): MultiHeadAttention(\n", " (heads): ModuleList(\n", " (0-3): 4 x Head(\n", " (key): Linear(in_features=64, out_features=16, bias=False)\n", " (query): Linear(in_features=64, out_features=16, bias=False)\n", " (value): Linear(in_features=64, out_features=16, bias=False)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (proj): Linear(in_features=64, out_features=64, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (ffwd): FeedFoward(\n", " (net): Sequential(\n", " (0): Linear(in_features=64, out_features=256, bias=True)\n", " (1): ReLU()\n", " (2): Linear(in_features=256, out_features=64, bias=True)\n", " (3): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (ln1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " (ln2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (1): Block(\n", " (sa): MultiHeadAttention(\n", " (heads): ModuleList(\n", " (0-3): 4 x Head(\n", " (key): Linear(in_features=64, out_features=16, bias=False)\n", " (query): Linear(in_features=64, out_features=16, bias=False)\n", " (value): Linear(in_features=64, out_features=16, bias=False)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (proj): Linear(in_features=64, out_features=64, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (ffwd): FeedFoward(\n", " (net): Sequential(\n", " (0): Linear(in_features=64, out_features=256, bias=True)\n", " (1): ReLU()\n", " (2): Linear(in_features=256, out_features=64, bias=True)\n", " (3): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (ln1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " (ln2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (2): Block(\n", " (sa): MultiHeadAttention(\n", " (heads): ModuleList(\n", " (0-3): 4 x Head(\n", " (key): Linear(in_features=64, out_features=16, bias=False)\n", " (query): Linear(in_features=64, out_features=16, bias=False)\n", " (value): Linear(in_features=64, out_features=16, bias=False)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (proj): Linear(in_features=64, out_features=64, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (ffwd): FeedFoward(\n", " (net): Sequential(\n", " (0): Linear(in_features=64, out_features=256, bias=True)\n", " (1): ReLU()\n", " (2): Linear(in_features=256, out_features=64, bias=True)\n", " (3): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (ln1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " (ln2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " )\n", " (3): Block(\n", " (sa): MultiHeadAttention(\n", " (heads): ModuleList(\n", " (0-3): 4 x Head(\n", " (key): Linear(in_features=64, out_features=16, bias=False)\n", " (query): Linear(in_features=64, out_features=16, bias=False)\n", " (value): Linear(in_features=64, out_features=16, bias=False)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (proj): Linear(in_features=64, out_features=64, bias=True)\n", " (dropout): Dropout(p=0.0, inplace=False)\n", " )\n", " (ffwd): FeedFoward(\n", " (net): Sequential(\n", " (0): Linear(in_features=64, out_features=256, bias=True)\n", " (1): ReLU()\n", " (2): Linear(in_features=256, out_features=64, bias=True)\n", " (3): Dropout(p=0.0, inplace=False)\n", " )\n", " )\n", " (ln1): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " (ln2): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " )\n", " )\n", " (ln_f): LayerNorm((64,), eps=1e-05, elementwise_affine=True)\n", " (lm_head): Linear(in_features=64, out_features=65, bias=True)\n", ")" ] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "code", "source": [ "# generate from the model\n", "context = torch.zeros((1, 1), dtype=torch.long, device=device)\n", "print(decode(loaded_model.generate(context, max_new_tokens=2000)[0].tolist()))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "m46OnNXq6PAV", "outputId": "e547525f-98c0-4355-92ef-559f6c2ba238" }, "execution_count": 18, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "Forntlefires, love the done, or all love tears\n", "That braud the strough.\n", "\n", "BUCHNIO:\n", "Is\n", "For that I hat deam throve? we parrlignos;\n", "My bregain minousiner mile into the doth,\n", "Warwien not his day hath;\n", "Whose basy touther ploudde metornies'drey would be themseremes to have\n", "You good accarm, menot wtoo cown:\n", "Is have mostil\n", "Before prunces.\n", "\n", "Speaking A-dught:\n", "Whow 'sile her fry hath acvionce,\n", "Your cange, side of-day; this I seep!\n", "Aher approve; I\n", "drumber, any till amberd, come it suffet nexwarrans\n", "To hear you that what art thim for a dish! Whiler not some men;\n", "Hareth, I am broth, thenese oof.\n", "Croth before wortune's hande and if brote\n", "Come andmitation it. Tentess I what\n", "That ascess Weringmans, te us;\n", "And your Servant-thy moime, that whose.\n", "\n", "CORIOLANUS:\n", "Now, stay to the resmorn?\n", "\n", "CRANGE:\n", "It have pleave to some, for soul;\n", "He fatelly here that you, hesseliemes five oldince\n", "Our confolle, too you stay'd my being to't,\n", "My lord I am then most the knows doot hid-gress.\n", "\n", "KING RICHARD GDITH:\n", "As beconsure! So youil heart fear; and whilook my arm verpast,\n", "And staven to fathy down I vir all prace,\n", "And be betcasion your balt, to draying and the bottchmy,\n", "The griake must worse it. As I have owle well I who stray good.\n", "\n", "My anviusice: andress unthat fonds of oad;\n", "ne's eye the notraing and timer cimmman:\n", "Heth lain. What's is the castad,\n", "And their speake fatwort off.\n", "\n", "Shy:\n", "What marry; thysele, time onge,\n", "And by bown, merpety to to of crive thou secam.\n", "\n", "QUEEN VINCENTIO:\n", "How my bold; good poson\n", "I finly torthus.\n", "Our you if your aware watly sweet\n", "On all fair livishts thee our then plast banoting\n", "What have duckn, so\n", "them the hostfeive.\n", "\n", "HIRDIO:\n", "\n", "GLOUCERDIO:\n", "And all capure Toncant mack.\n", "\n", "CAPULET:\n", "O mean bodams'd my tone thy wralf thee wilth\n", "And rencrown prow my ear them lovery\n", "Coringlike hath in recond:\n", "You will you from of God their all and not mine:\n", "With doess be Sives?\n", "So regort it thy mart solued sgaft world of him,\n", "What'st in else agged namfutiol.\n", "\n", "ANGENO:\n", "For that it you briave lay to your unpalssi\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "2GpnegQc8A9R" }, "execution_count": null, "outputs": [] } ] }