""" HuggingFace Spaces App for GPT-2 124M Shakespeare Model """ import torch import torch.nn as nn from torch.nn import functional as F import tiktoken import gradio as gr import math from dataclasses import dataclass 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) self.c_proj = nn.Linear(config.n_embd, config.n_embd) self.c_proj.NANOGPT_SCALE_INIT = 1 self.n_head = config.n_head self.n_embd = config.n_embd 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() qkv = self.c_attn(x) q, k, v = qkv.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) att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1))) att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf')) att = F.softmax(att, dim=-1) y = att @ v y = y.transpose(1, 2).contiguous().view(B, T, C) y = self.c_proj(y) return y class MLP(nn.Module): def __init__(self, config): super().__init__() self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd) self.gelu = nn.GELU(approximate='tanh') self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd) self.c_proj.NANOGPT_SCALE_INIT = 1 def forward(self, x): x = self.c_fc(x) x = self.gelu(x) x = self.c_proj(x) return x class Block(nn.Module): def __init__(self, config): super().__init__() self.ln_1 = nn.LayerNorm(config.n_embd) self.attn = CausalSelfAttention(config) self.ln_2 = nn.LayerNorm(config.n_embd) self.mlp = MLP(config) def forward(self, x): x = x + self.attn(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x @dataclass class GPTConfig: block_size: int = 1024 vocab_size: int = 50257 n_layer: int = 12 n_head: int = 12 n_embd: int = 768 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), h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]), ln_f=nn.LayerNorm(config.n_embd), )) self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False) self.transformer.wte.weight = self.lm_head.weight def forward(self, idx, targets=None): B, T = idx.size() assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}" pos = torch.arange(0, T, dtype=torch.long, device=idx.device) pos_emb = self.transformer.wpe(pos) tok_emb = self.transformer.wte(idx) x = tok_emb + pos_emb for block in self.transformer.h: x = block(x) x = self.transformer.ln_f(x) logits = self.lm_head(x) loss = None if targets is not None: loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1)) return logits, loss # Load model print("Loading model...") device = 'cuda' if torch.cuda.is_available() else 'cpu' config = GPTConfig() model = GPT(config) # Try to load model (works both locally and on HuggingFace) try: checkpoint = torch.load('model_checkpoint_final.pt', map_location=device) model.load_state_dict(checkpoint['model_state_dict']) print("Model loaded from checkpoint") except FileNotFoundError: print("Warning: Model checkpoint not found. Using untrained model.") # Model will be randomly initialized - not ideal but won't crash model.to(device) model.eval() print(f"Model ready on {device}") enc = tiktoken.get_encoding('gpt2') def generate_text(prompt, max_new_tokens=100, temperature=0.8, top_k=50): """Generate text from prompt""" try: # Encode prompt tokens = enc.encode(prompt) tokens = torch.tensor(tokens, dtype=torch.long, device=device).unsqueeze(0) # Generate with torch.no_grad(): for _ in range(max_new_tokens): # Forward pass logits, _ = model(tokens) logits = logits[:, -1, :] / temperature # Top-k sampling topk_probs, topk_indices = torch.topk(F.softmax(logits, dim=-1), top_k, dim=-1) ix = torch.multinomial(topk_probs, 1) next_token = torch.gather(topk_indices, -1, ix) # Append to sequence tokens = torch.cat([tokens, next_token], dim=1) # Stop if we hit max length if tokens.size(1) >= config.block_size: break # Decode generated_text = enc.decode(tokens[0].tolist()) return generated_text except Exception as e: return f"Error: {str(e)}" # Create Gradio interface with gr.Blocks(title="GPT-2 124M Shakespeare Model") as demo: gr.Markdown(""" # 🎭 GPT-2 124M Shakespeare Language Model This is a 124M parameter decoder-only transformer model trained on Shakespeare's complete works. **Training Results:** - Final Loss: 0.095127 (Target: < 0.099999) ✅ - Model Parameters: 124.44M - Training Steps: 1,637 Enter a prompt below to generate Shakespeare-style text! """) with gr.Row(): with gr.Column(): prompt_input = gr.Textbox( label="Prompt", placeholder="Enter your prompt here (e.g., 'First Citizen:', 'ROMEO:', 'To be or not')", value="First Citizen:", lines=3 ) max_tokens = gr.Slider( label="Max Tokens", minimum=50, maximum=200, value=100, step=10 ) temperature = gr.Slider( label="Temperature", minimum=0.1, maximum=2.0, value=0.8, step=0.1 ) top_k = gr.Slider( label="Top-K", minimum=10, maximum=100, value=50, step=10 ) generate_btn = gr.Button("Generate", variant="primary") with gr.Column(): output = gr.Textbox( label="Generated Text", lines=10, interactive=False ) # Example prompts gr.Markdown("### Example Prompts:") examples = gr.Examples( examples=[ ["First Citizen:"], ["ROMEO:"], ["To be or not"], ["HAMLET:"], ["MACBETH:"], ], inputs=prompt_input ) generate_btn.click( fn=generate_text, inputs=[prompt_input, max_tokens, temperature, top_k], outputs=output ) gr.Markdown(""" --- **Note:** The model was trained on Shakespeare text and generates text in that style. Generated text may not always be coherent but should follow Shakespearean patterns. """) if __name__ == "__main__": demo.launch(share=True)