import gradio as gr import gdown import torch import torch.nn as nn from torch.nn import functional as F device = "cuda" if torch.cuda.is_available() else "cpu" n_embd = 384 n_head = 4 n_layer = 4 block_size = 128 dropout = 0.2 gdown.download('https://drive.usercontent.google.com/download?id=14k2xUrvJ32trhLCzV2_O7klreBBA3dUu&authuser=0&confirm=t', 'model.pth', quiet=False) gdown.download('https://drive.usercontent.google.com/download?id=1-JSvTzTxyI5zJwO39o0wuxJpvY-NqzGE&export=download&authuser=0&confirm=t&uuid=9eff48e6-67f8-4728-aa7f-552c497fb02c&at=AN_67v0xah9SgNOs5FDNKIuxVWL9%3A1727637766874', 'data.txt.gz', quiet=False) import gzip with gzip.open('data.txt.gz', 'rt', encoding='utf-8') as f: dataset = f.read() # chars = sorted(list(set(dataset))) chars = ['\t', '\n', ' ', '!', '"', '#', '$', '%', '&', "'", '(', ')', '*', '+', ',', '-', '.', '/', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', ':', ';', '<', '=', '>', '?', '@', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '[', '\\', ']', '^', '_', '`', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', '{', '|', '}', '~', '\x81', '\x8d', '\x8f', '\x90', '\x92', '\x93', '\x94', '\x9d', '\xa0', '¡', '¢', '£', '¤', '¥', '¦', '§', '¨', '©', 'ª', '«', '¬', '\xad', '®', '¯', '°', '±', '²', '³', '´', 'µ', '¶', '·', '¸', '¹', 'º', '»', '¼', '½', '¾', '¿', 'Â', 'Ã', 'Æ', 'Ç', 'É', 'Ê', 'Ë', 'Ð', 'Ò', '×', 'Ø', 'Ù', 'à', 'á', 'â', 'ã', 'ä', 'å', 'é', 'í', 'ï', 'ð', 'ñ', 'ó', 'ö', 'ā', 'Œ', 'œ', 'Š', 'š', 'Ÿ', 'Ž', 'ž', 'ƒ', 'ˆ', '˜', 'і', '\u2005', '\u2009', '\u200a', '\u200b', '\u200e', '–', '—', '―', '‘', '’', '‚', '“', '”', '„', '†', '‡', '•', '…', '\u2028', '\u2029', '\u202a', '‰', '′', '‹', '›', '€', '™', '−', '─', '」', 'fi', '\ufeff', '�', '𝑐', '🌴', '🌹', '🍌', '🙂'] vocab_size = 212# len(chars) string_to_int = { ch:i for i,ch in enumerate(chars) } int_to_string = { i:ch for i,ch in enumerate(chars) } encode = lambda s: [string_to_int[c] for c in s] decode = lambda l: ''.join([int_to_string[i] for i in l]) data = torch.tensor(encode(dataset), dtype=torch.long) class Head(nn.Module): 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) q = self.query(x) w = q @ k.transpose(-2, -1) * C**-0.5 w = w.masked_fill(self.tril[:T, :T] == 0, float("-inf")) w = F.softmax(w, dim=-1) w = self.dropout(w) v = self.value(x) out = w @ v return out class MultiHeadAttention(nn.Module): 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): 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): def __init__(self, n_embd, n_head): 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, vocab_size): super().__init__() 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) self.lm_head = nn.Linear(n_embd, vocab_size) def forward(self, idx, targets=None): B, T = idx.shape tok_emb = self.token_embedding_table(idx) pos_emb = self.position_embedding_table(torch.arange(T, device=device)) x = tok_emb + pos_emb x = self.blocks(x) x = self.ln_f(x) logits = self.lm_head(x) 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): for _ in range(max_new_tokens): idx_cond = idx[:, -block_size:] logits, loss = self(idx_cond) logits = logits[:, -1, :] probs = F.softmax(logits, dim=-1) idx_next = torch.multinomial(probs, num_samples=1) idx = torch.cat((idx, idx_next), dim=1) return idx @torch.no_grad() def estimate_loss(): out = {} model.eval() for split in ["training", "validation"]: 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 model = GPTLanguageModel(vocab_size) m = model.to(device) # print(sum(p.numel() for p in m.parameters()) / 1e3, "K parameters") # load the model.pth model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False) model.eval() def respond( message, max_tokens=512, ): context = torch.tensor(encode(message), dtype=torch.long, device=device).unsqueeze( 0 ) response = decode(model.generate(context, max_new_tokens=max_tokens)[0].tolist()) return response iface = gr.Interface( fn=respond, inputs=[ gr.Textbox(lines=5, label="Message", value="Hi Harry Potter"), gr.Slider(minimum=100, maximum=2048, value=256, label="Max Tokens"), ], outputs="text", title="PotterLLM", description="A language model trained on Harry Potter Series.", theme="huggingface", ) if __name__ == "__main__": iface.launch()