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app.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import gradio as gr
+import tiktoken
+import math
+from dataclasses import dataclass
+
+class LayerNorm(nn.Module):
+    def __init__(self, ndim, bias):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(ndim))
+        self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None
+    def forward(self, x):
+        return F.layer_norm(x, self.weight.shape, self.weight, self.bias, 1e-5)
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        self.attn_dropout = nn.Dropout(config.dropout)
+        self.resid_dropout = nn.Dropout(config.dropout)
+        self.n_head, self.n_embd = config.n_head, config.n_embd
+        self.flash = hasattr(F, 'scaled_dot_product_attention')
+        if not self.flash:
+            self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size))
+                                       .view(1, 1, config.block_size, config.block_size))
+
+    def forward(self, x):
+        B, T, C = x.size()
+        q, k, v  = self.c_attn(x).split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2)
+        y = F.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=True)
+        y = y.transpose(1, 2).contiguous().view(B, T, C)
+        return self.resid_dropout(self.c_proj(y))
+
+class MLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd, bias=config.bias)
+        self.gelu    = nn.GELU()
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
+        self.dropout = nn.Dropout(config.dropout)
+    def forward(self, x):
+        return self.dropout(self.c_proj(self.gelu(self.c_fc(x))))
+
+class Block(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.ln1 = LayerNorm(config.n_embd, config.bias)
+        self.attn = CausalSelfAttention(config)
+        self.ln2 = LayerNorm(config.n_embd, config.bias)
+        self.mlp = MLP(config)
+    def forward(self, x):
+        x = x + self.attn(self.ln1(x))
+        x = x + self.mlp(self.ln2(x))
+        return x
+
+@dataclass
+class GPTConfig:
+    block_size: int = 256
+    vocab_size: int = 50257
+    n_layer: int = 6
+    n_head: int = 6
+    n_embd: int = 384
+    dropout: float = 0.0
+    bias: bool = True
+
+class GPT(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.transformer = nn.ModuleDict(dict(
+            wte = nn.Embedding(config.vocab_size, config.n_embd),
+            wpe = nn.Embedding(config.block_size, config.n_embd),
+            drop = nn.Dropout(config.dropout),
+            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f = LayerNorm(config.n_embd, config.bias),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        self.transformer.wte.weight = self.lm_head.weight
+        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):
+        device = idx.device
+        b, t = idx.size()
+        pos = torch.arange(0, t, dtype=torch.long, device=device)
+        tok_emb = self.transformer.wte(idx)
+        pos_emb = self.transformer.wpe(pos)
+        x = self.transformer.drop(tok_emb + pos_emb)
+        for block in self.transformer.h:
+            x = block(x)
+        x = self.transformer.ln_f(x)
+        logits = self.lm_head(x[:, [-1], :])
+        return logits, None
+
+    @torch.no_grad()
+    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
+        for _ in range(max_new_tokens):
+            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
+            logits, _ = self(idx_cond)
+            logits = logits[:, -1, :] / temperature
+            if top_k is not None:
+                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                logits[logits < v[:, [-1]]] = -float('Inf')
+            probs = F.softmax(logits, dim=-1)
+            idx_next = torch.multinomial(probs, num_samples=1)
+            idx = torch.cat((idx, idx_next), dim=1)
+        return idx
+
+device = 'cpu'
+enc = tiktoken.get_encoding("gpt2")
+
+config = GPTConfig(
+    vocab_size=50257,
+    block_size=128,
+    n_layer=6,
+    n_head=6,
+    n_embd=384,
+    dropout=0.1,
+    bias=True
+)
+
+model = GPT(config)
+model.load_state_dict(torch.load('C:/Users/91981/OneDrive/Desktop/ai project/My-Project-Folder/best_model_params.pt', map_location=device))
+model.eval()
+model.to(device)
+
+def generate_story(prompt, max_new_tokens=100, temperature=0.8):
+    start_ids = enc.encode_ordinary(prompt)
+    x = torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...]
+    y = model.generate(x, max_new_tokens=int(max_new_tokens), temperature=temperature, top_k=200)
+    return enc.decode(y[0].tolist())
+
+article = """
+<div style="text-align: center; margin-top: 20px;">
+    <p>
+        This model was trained on the <strong>TinyStories dataset</strong> and uses a GPT-style architecture to generate new stories from a starting prompt.
+    </p>
+    <p>
+        <strong>Created by:</strong> <a href="https://www.linkedin.com/in/harshitchawla4705/" target="_blank">Harshit Chawla</a>
+    </p>
+    <hr>
+    <h4>Relevant Research Papers</h4>
+    <p>
+        <a href="https://arxiv.org/abs/2305.07759" target="_blank">TinyStories: How Small Can Language Models Be and Still Speak Coherent English?</a><br>
+        <a href="https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf" target="_blank">Language Models are Unsupervised Multitask Learners (GPT-2)</a><br>
+        <a href="https://arxiv.org/abs/1706.03762" target="_blank">Attention Is All You Need</a>
+    </p>
+</div>
+"""
+
+iface = gr.Interface(
+   fn=generate_story,
+   inputs=[
+       gr.Textbox(lines=3, placeholder="E.g., Once upon a time, there was a little red fox..."),
+       gr.Slider(minimum=20, maximum=500, step=10, value=150, label="Number of Words to Generate"),
+       gr.Slider(minimum=0.1, maximum=1.5, step=0.1, value=0.8, label="Creativity (Temperature)")
+   ],
+   outputs=gr.Textbox(label="Generated Story", lines=20),
+   title="📖 AI Story Generator",
+   description="Enter a sentence to start a story and see what the AI writes next! Adjust the sliders to control the length and creativity of the story.",
+   article=article,
+   theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"),
+   allow_flagging="never",
+   examples=[
+       ["Once upon a time, a curious cat named Whiskers", 150, 0.7],
+       ["In a land filled with candy, a brave gingerbread man", 200, 0.9],
+       ["A lonely robot sat on a hill, looking at the stars", 100, 0.5]
+   ]
+)
+
+if __name__ == "__main__":
+    iface.launch()

best_model_params.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c28b0a004e6c7a01156e82f03a7140903ac75188f74ff81cf28f1bfe7fca7459
+size 120011315