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app.py

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+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+import numpy as np
+import random
+import re
+import gradio as gr
+
+# hyperparameters
+batch_size = 16 # how many independent sequences will we process in parallel?
+block_size = 32 # what is the maximum context length for predictions?
+max_iters = 5000
+eval_interval = 100
+learning_rate = 1e-3
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+eval_iters = 200
+n_embd = 64
+n_head = 4
+n_layer = 4
+dropout = 0.0
+# ------------
+
+torch.manual_seed(1337)
+
+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):
+        B,T,C = x.shape
+        k = self.key(x)   # (B,T,C)
+        q = self.query(x) # (B,T,C)
+        # compute attention scores ("affinities")
+        wei = q @ k.transpose(-2,-1) * C**-0.5 # (B, T, C) @ (B, C, 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,C)
+        out = wei @ v # (B, T, T) @ (B, T, C) -> (B, T, C)
+        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(n_embd, 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
+
+# super simple bigram model
+class BigramLanguageModel(nn.Module):
+    def __init__(self, dataset_text, n_embd):
+        super().__init__()
+
+        # Compute character-related parameters
+        self.chars = sorted(list(set(dataset_text)))
+        self.vocab_size = len(self.chars)
+        self.stoi = {ch: i for i, ch in enumerate(self.chars)}
+        self.itos = {i: ch for ch, i in self.stoi.items()}
+
+        self.token_embedding_table = nn.Embedding(self.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)
+        self.lm_head = nn.Linear(n_embd, self.vocab_size)
+        self.encode = lambda s: [self.stoi[c] for c in s] # encoder: take a string, output a list of integers
+        self.decode = lambda l: ''.join([self.itos[i] for i in l]) # decoder: take a list of integers, output a string
+
+        
+    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
+
+# Reading shakespeare data
+with open('input.txt', 'r', encoding='utf-8') as f:
+    shakespeare_text = f.read()
+
+# Load the shakespeaere model
+shakespeare_model = BigramLanguageModel(shakespeare_text, n_embd).to(device)  # Initialize an instance of your model
+shakespeare_model.load_state_dict(torch.load('shakespeaere_language_model.pth', map_location=torch.device('cpu')))
+shakespeare_model.eval()  # Set the model to evaluation mode
+
+
+def generate_shakespeare_outputs(prompt=None, max_new_tokens=2000):
+  if prompt:
+    context = torch.tensor(shakespeare_model.encode(prompt), dtype=torch.long, device=device).view(1, -1)
+  else:
+    context = torch.zeros((1, 1), dtype=torch.long, device=device)
+  text_output = shakespeare_model.decode(shakespeare_model.generate(context, max_new_tokens=max_new_tokens)[0].tolist())
+  return text_output
+
+title = "Nano GPT using Shakespeare Data"
+
+description1 = "Nano GPT trained on <a href='https://www.kaggle.com/datasets/mikeortman/wikipedia-sentences'>Shakespeare dataset</a>. It is trained on a very small amount of data to understand how GPT's are trained and built. The implementation can be found <a href='https://github.com/karpathy/nanoGPT'>here.</a>"
+
+demo = gr.Interface(generate_shakespeare_outputs,
+                    inputs=[gr.Textbox(label="Enter any prompt ", type="text", value="Once upon a time,"),
+                            gr.Slider(minimum=100, maximum=5000, step=100, value=2000, label="Max new tokens")],
+                    outputs=gr.Textbox(label="Output generated", type="text"), description=description1)
+
+demo.launch()

nano-gpt.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:129463e9593a0774dcb23ad679d4e31bb381e47c508f37ff782cb10f0c32b48e
+size 945416

requirements.txt

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+torch
+gradio