commit

app.py

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+import gradio as gr
+from model import *
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = GPTLanguageModel().to(DEVICE)
+model.load_state_dict(torch.load("mini-gpt.pth",map_location=DEVICE), strict=False)
+model.eval()
+answer = decode(model.generate(context, max_new_tokens=500)[0].tolist())
+
+def display(text,number):
+    combined_text = text + answer[:number + 1]
+    return combined_text
+
+input_box = gr.Textbox(label="Story Lines",value="Once Upon a Time")
+input_slider = gr.Slider(minimum=200, maximum=500, label="Select the maxium number of tokens/words:",step=100)
+output_text = gr.Textbox() 
+gr.Interface(fn=display, inputs=[input_box,input_slider], outputs=output_text).launch()

mini-gpt.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8c1c86b050a99e05dd53d95f6aff1ddc5773e5d35372916f920edbfecb747797
+size 52658082

model.py

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+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+
+# hyperparameters
+batch_size = 64 # how many independent sequences will we process in parallel?
+block_size = 256 # what is the maximum context length for predictions?
+max_iters = 5000
+eval_interval = 500
+learning_rate = 3e-4
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+eval_iters = 200
+n_embd = 384
+n_head = 6
+n_layer = 6
+dropout = 0.2
+# ------------
+
+torch.manual_seed(1337)
+
+# wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
+with open('input.txt', 'r', encoding='utf-8') as f:
+    text = f.read()
+
+# here are all the unique characters that occur in this text
+chars = sorted(list(set(text)))
+vocab_size = len(chars)
+# create a mapping from characters to integers
+stoi = { ch:i for i,ch in enumerate(chars) }
+itos = { i:ch for i,ch in enumerate(chars) }
+encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
+decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
+
+# Train and test splits
+data = torch.tensor(encode(text), dtype=torch.long)
+n = int(0.9*len(data)) # first 90% will be train, rest val
+train_data = data[:n]
+val_data = data[n:]
+
+# data loading
+def get_batch(split):
+    # generate a small batch of data of inputs x and targets y
+    data = train_data if split == 'train' else val_data
+    ix = torch.randint(len(data) - block_size, (batch_size,))
+    x = torch.stack([data[i:i+block_size] for i in ix])
+    y = torch.stack([data[i+1:i+block_size+1] for i in ix])
+    x, y = x.to(device), y.to(device)
+    return x, y
+
+@torch.no_grad()
+def estimate_loss():
+    out = {}
+    model.eval()
+    for split in ['train', 'val']:
+        losses = torch.zeros(eval_iters)
+        for k in range(eval_iters):
+            X, Y = get_batch(split)
+            logits, loss = model(X, Y)
+            losses[k] = loss.item()
+        out[split] = losses.mean()
+    model.train()
+    return out
+
+class Head(nn.Module):
+    """ one head of self-attention """
+
+    def __init__(self, head_size):
+        super().__init__()
+        self.key = nn.Linear(n_embd, head_size, bias=False)
+        self.query = nn.Linear(n_embd, head_size, bias=False)
+        self.value = nn.Linear(n_embd, head_size, bias=False)
+        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        # input of size (batch, time-step, channels)
+        # output of size (batch, time-step, head size)
+        B,T,C = x.shape
+        k = self.key(x)   # (B,T,hs)
+        q = self.query(x) # (B,T,hs)
+        # compute attention scores ("affinities")
+        wei = q @ k.transpose(-2,-1) * k.shape[-1]**-0.5 # (B, T, hs) @ (B, hs, T) -> (B, T, T)
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf')) # (B, T, T)
+        wei = F.softmax(wei, dim=-1) # (B, T, T)
+        wei = self.dropout(wei)
+        # perform the weighted aggregation of the values
+        v = self.value(x) # (B,T,hs)
+        out = wei @ v # (B, T, T) @ (B, T, hs) -> (B, T, hs)
+        return out
+
+class MultiHeadAttention(nn.Module):
+    """ multiple heads of self-attention in parallel """
+
+    def __init__(self, num_heads, head_size):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])
+        self.proj = nn.Linear(head_size * num_heads, n_embd)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)
+        out = self.dropout(self.proj(out))
+        return out
+
+class FeedFoward(nn.Module):
+    """ a simple linear layer followed by a non-linearity """
+
+    def __init__(self, n_embd):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),
+            nn.ReLU(),
+            nn.Linear(4 * n_embd, n_embd),
+            nn.Dropout(dropout),
+        )
+
+    def forward(self, x):
+        return self.net(x)
+
+class Block(nn.Module):
+    """ Transformer block: communication followed by computation """
+
+    def __init__(self, n_embd, n_head):
+        # n_embd: embedding dimension, n_head: the number of heads we'd like
+        super().__init__()
+        head_size = n_embd // n_head
+        self.sa = MultiHeadAttention(n_head, head_size)
+        self.ffwd = FeedFoward(n_embd)
+        self.ln1 = nn.LayerNorm(n_embd)
+        self.ln2 = nn.LayerNorm(n_embd)
+
+    def forward(self, x):
+        x = x + self.sa(self.ln1(x))
+        x = x + self.ffwd(self.ln2(x))
+        return x
+
+class GPTLanguageModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        # each token directly reads off the logits for the next token from a lookup table
+        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)
+        self.position_embedding_table = nn.Embedding(block_size, n_embd)
+        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])
+        self.ln_f = nn.LayerNorm(n_embd) # final layer norm
+        self.lm_head = nn.Linear(n_embd, vocab_size)
+
+        # better init, not covered in the original GPT video, but important, will cover in followup video
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+
+        # idx and targets are both (B,T) tensor of integers
+        tok_emb = self.token_embedding_table(idx) # (B,T,C)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=device)) # (T,C)
+        x = tok_emb + pos_emb # (B,T,C)
+        x = self.blocks(x) # (B,T,C)
+        x = self.ln_f(x) # (B,T,C)
+        logits = self.lm_head(x) # (B,T,vocab_size)
+
+        if targets is None:
+            loss = None
+        else:
+            B, T, C = logits.shape
+            logits = logits.view(B*T, C)
+            targets = targets.view(B*T)
+            loss = F.cross_entropy(logits, targets)
+
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens):
+        # idx is (B, T) array of indices in the current context
+        for _ in range(max_new_tokens):
+            # crop idx to the last block_size tokens
+            idx_cond = idx[:, -block_size:]
+            # get the predictions
+            logits, loss = self(idx_cond)
+            # focus only on the last time step
+            logits = logits[:, -1, :] # becomes (B, C)
+            # apply softmax to get probabilities
+            probs = F.softmax(logits, dim=-1) # (B, C)
+            # sample from the distribution
+            idx_next = torch.multinomial(probs, num_samples=1) # (B, 1)
+            # append sampled index to the running sequence
+            idx = torch.cat((idx, idx_next), dim=1) # (B, T+1)
+        return idx
+
+model = GPTLanguageModel()
+m = model.to(device)
+context = torch.zeros((1, 1), dtype=torch.long, device=device)

more.txt

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+
+I am sound to do for a king sleep:
+I came to convert thy grief; and then be thieve
+My indictment state and heart my soldier;
+Some thy fable of life is flat, to woo.
+
+ESCALUS:
+Learn's is that, and but that thy, by edict.
+
+POLIXENES:
+Your tongue, my lord.
+If you did mean they will this bud most know two:
+if you wish met; but that they smoth were noted trainful
+doing the one, and they stand goods for
+minemen, know not at such receivity to me
+welcome tof what's seen men.
+
+Shepherd:
+Out of this, night, if thou!
+
+ESCALUS:
+What are the prince, happy neck of his passes.
+
+POMPHEY:
+Then what make, fit shore sound for some requish,
+short this he hath done; would afflict him the mock.
+
+ANGELO:
+Go weep, my lords. Come, come hither, thy absent,
+Show'd thy frail and mock, and sworn break thirt.
+
+POMPEY:
+Since may, while you be glad and so swift ere then
+come to seek me the flow sighting, so bald. Pray,
+such forth as I can be as old. Let me come, follow.
+
+BENVOLIO:
+Here comes bleed, Johve ajoy, bear a baptaiet,
+pa, you'll malk on the widow look.
+
+MISTANLEY:
+How, my lord, that's the better Marcius!
+
+POMPEY:
+W goes here as Hallybamer than oath.
+Whate's first? is it your lord is fast?
+
+MAMILLIUS:
+O, come, help your bed:
+Come by that baits you off, that I shall rest advise
+By the kind and his courtesy from him,
+Now how shall in the promison and unDan oate
+Not part way to a way for wholesomen eye
+May as in one. This is the issue of truth:
+When then fortune with untimely her hence,
+Why tretth nurse the father. Ha! how
+say your husban is sworn, I say!
+For Rome hence, give me already.
+
+ELBUNVALEN:
+Gentle youth,
+Good vister; you; call it.
+
+LUCIO:
+This is the captain which hath not seat you upon.
+
+Lord, Servingman:
+If when the dutest deditar, you are reckoned,
+your hum, as do cloud as you in 's,
+You know me from my worth, I hear my sweet son.
+
+HORTENSIO:
+At shall's tuf it so?
+
+GREMIO:
+Nay, but, indeed, he's sent me 'past with him.
+
+Boy:
+I tell your lassage: 'tis true, she's heart; e'tis mad.
+
+Third Gentleman:
+An a priest, that's no many of a stage.
+
+GREY:
+You are a dear mother; whisp'st I return,
+Now youth; a mind of stricks 'O, that they may
+not venture in the war; your time may move, nothing
+which never may shall never bring
+from me away the loss of men.
+
+Second Watchman:
+Coment, but come! muain;
+yet is a letter be my lording. Nay, sir;
+why, first you are the worst call your condity, as
+hallong incled in loob the; never easy of them
+wondering you depend, I cannot be absent forbated.
+He could fence his sin, a wound in this ease, thou
+wastted him to haunt this formwarned: which is my country, I am
+general, and a childish curch-dook in the sheat
+she, though it were a pited men! why, be it not,
+you shall, withhout the blush, they thanks it me for
+man for this action was to mededlar.
+
+LUCIO:
+Give me no longer to any thing. If you think thou
+shoulders he would Prictor the rest.
+
+HORTENSIO:
+But what tables robe great dinsinger, in a thousand robbers?
+
+GREM:
+Tell him where is Barnardine? they are in prepetty thing.
+If we have with heinous is not leven
+now your justice in the wars then desirer
+most to be some powdeed, that he doth show the bald
+which yot whip: you have made no more to lean ince,
+and current to come.
+
+POLIXENES:
+O, let it be:
+Let smile it hold.
+
+PETRUCHIO:
+Good Angelo, did give me whip agreat a
+very way, stirring night?
+
+ThONTAGUE:
+Ay, my lord, pretty passion, for means.
+There long I see the March bepossed of a sin,
+And prince in a war shame about them;
+Which, threld never shall
+Then close me but this, and make pray of proceed.
+
+GRUCHIO:
+His protethinping; but say you, how do
+creft it, It was done in behalf which do so,
+Which slacking or bout, which dish yours, brother?
+
+GREMIO:
+Well, indeed, to show me what so thyself.
+
+TRANIO:
+Leasurence, what am I absent, friar?
+
+BIONDENELO:
+I am know 'tis advantaged; and in that.
+
+TRANIO:
+That if once live, then I suppen my heart.
+But canst me, sir?
+
+GRUMIO:
+
+LUCENTIO:
+Groat? how is Ah, sir?
+
+TRANIO:
+Too the bawd, is't born?
+
+BANARifRa-love, he beging to severe clape.
+
+TRANIO:
+My house is it fa mad stand beg; I am against
+By Ceternal making I prick-bawd. Then will make stay
+As in any tires from and of thirst breath, we did
+will mend again; they have confess me to use
+As mine enemy to notice.
+
+POMPEY:
+Why give me leave?
+
+HERMIONE:
+There lies.
+
+MISTRESS OVERDONE:
+That have you sad.
+
+POMPEY:
+Come, sir; you warry non upon the spoil.
+
+POMPEY:
+By offer, buy what?
+
+MOPEY:
+Exchanging is forth, sir, I willing.
+Eleasand, that with your good worship, on the
+very table, thing to have vailed them back that
+brgoat o'er.
+
+ESCALUS:
+Then rusty till he in thy prison news man: it indeed
+this one I degree, yea in in commity means in
+and what yet pomposes. There is resorse yet more ternier than
+the nobilinester love than one that he she hath got gross; he
+stood retrue, and therefore, with her two a
+rich loved to pie circles in the pocky of dowry: he
+is renowned, if he not coulest home be prosperfer.
+
+Shepherd:
+What, you think, how you will, my instant the
+sworn, the wounds your weary that he spoke with
+sworth cluckes; having not yet there no councile without of him
+hour, with she winher mess to this offence the king.
+Ga. How do I ghink thee, foolish for the pliffer?
+But what's now, thine are nost? What never good Sir
+To Richmond?
+
+SAMPSON:
+What unto this?
+
+GREGORY:
+My good lords, which do he returner should?
+
+SAMPSON:
+Is the grainted of the Capulets! Come, good my hountsmen;
+there's no dishonoured gost on the mutinon; a sensible,
+A child's neat, with why he bast in't.
+
+GRUMIO:
+I thank your most shadow make a poar maid
+Betwear reason where I was best.
+
+TRANA:
+Give me awake, master, a master of your needs.
+
+Propost:
+Good for joy, good Prince, but on brinch and wood
+ladies, were he to bed merry!
+
+Provost:
+Give me in justice, to save this world; let her.
+
+DUKE VINCENTIO:
+Richard an old you.
+
+Prithee, Prithete, right.
+
+DUKE VINCENTIO:
+Well, well metter you than a trick.
+
+CLAUS:
+What, who
+most are you? Let Aufidius?
+
+CLOMINLUS:
+If, an it like your deual to content;
+Which, if we are here was lawful, your weekin friends
+To you and believe or the bads 'forehead?
+
+DUKE VINCENTIO:
+Sleep the warrants, thou know this duke?
+
+ESCALUS:
+For so see, let him, I'll conquest; you will entain.
+
+Provost:
+Go, know your husband, for an oath will, think you he'll
+have here a pertaisite for your misdeeds.
+But what where you unhacking to your brother?
+
+Provost:
+Your mother affection shall fault for me?
+
+MARIANA:
+No, I'll know I see that; my babe it that slat,
+Your subjectanets, your misa grant to such
+As liquoth throw toward him to soath a
+More great to my whole at home kindness:
+But this naked, we'ld to
+reason what looks that the vantages; but tell you,
+which since lay these to the old maiden ass you, if
+I were such pride,
+whom you mean's in qual of yourself, or knowledge
+your general.
+
+First Senator:
+He's good?
+
+MENENIUS:
+Is't less.
+
+First Senator:
+Said, that's too for Rome that wounds morning
+friendship, He that you foe, have lead'st
+To Chepherd Peterdition's restlest top,
+She decline our good willingless not now, or never son
+Most holy fornights, friendly, deserved it you;
+For in the deep the rebes expectly,
+For that as the thought of is sharp would
+Think what 'twas he, though a short, ye're a kindred
+To make her good night. Good Crioli, sir;
+Apast good breed of my son! God forbid her hence!
+
+Second Murderer:
+Go, cousin, my lord, good my lws.
+
+ABHORSON:
+God give me look, in my town word!
+Here is Montague; and, doubt not great men's wre,
+That itself and might came in promise-proclaim.
+
+Secival Servingman:
+What's he? here Rile and a Roman, against my tongue
+and the ripe of Proces to ta'en the worst, and, in grace
+mattering; presses him, insquire, and child, 'tis such
+dish with a gentleman; a pleasy beggar-beter stripe.
+where strong you here?
+
+Second Servingman:
+Ye, if he should be general, rest by the challest enemies?
+
+Servant:
+Ye ne$, sir by Paduio's butt.
+
+MARCIUS:
+Let all, I know no more years commands.
+
+LARTIUS:
+
+MARCIUS:
+Let's him in.
+
+Second Soldier:
+He's once take a widow, having up with a slove;
+And that shouts, considering him, and that
+knew his soul to his good and told his pin.
+
+V'JIwN:
+Would to Barning, that's thus?
+
+Second Servingman:
+Ay, sir, then, to-morrow.
+
+Cld Sirrana!
+
+ANGELO:
+Go tell? If this turns who in you? Ladde
+Lord Master Angelo, what I think, who strike
+deceived to Bianca, is eleven of Edward's head?
+Even for a ridsman; my secury maid
+have princed this, and eat will a gentleman to you. If
+you are a braith the lir-dile, be it yet fit your
+disings and less affect you
+of your unders! any foot you were as flaw's, all
+the hence of of the goose and whate you thing be
+done, but your think, if you'll be,--
+
+Murry country, saying so, cleave you, sir,
+To have that sensel in your temples; let it to speak;
+which your integrion this counterfeit of a
+desire in affect.
+
+ISABELLA:
+Is it that?
+
+LUCIO:
+Sawing a white poison! He Sjul wrongs upon you;
+And droth the utterneysty rest, and so die you.
+How now! who's kitchly him, for his body?
+
+DUKE VINCENTIO:
+Now, good believe you!
+If if you be so, already, let us have not
+To grieve your tenenant time to be youk. Down, sir, betroth,
+And devise the buttler, young Baptista's
+deedsiter, and stire the king you so hot!
+But his trift here, he should obsend,
+The sacred Trob his constant: he was wont,
+A doubtle credition, and aught of ninex,
+Did like amplift; stand the stenators, deputy honour.
+My cousin, why shakest, is it gone?
+
+BENVOLIO:
+Parison, how I'll undertake it! if it be
+-as is toubt any teddlescer? O here have very we,
+Enter let, Hermione, thus that Romeo dearly,
+I'll with't. This empery please what she
+herself distress life, gentle which should recove no
+cure, to the knaves, he would show profess them
+the worst have but her to this witte.
+
+JULIET:
+How would leave Grace to the yield?
+
+Nurse:
+And mine, mistress!
+
+LADY CAPULET:
+Good Montague! O, poor boy, proud blest!
+orge her c

requirements.txt

@@ -0,0 +1,2 @@
+torch
+gradio