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import gradio as gr |
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import gdown |
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import torch |
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import torch.nn as nn |
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from torch.nn import functional as F |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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n_embd = 384 |
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n_head = 4 |
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n_layer = 4 |
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block_size = 128 |
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dropout = 0.2 |
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gdown.download('https://drive.usercontent.google.com/download?id=14k2xUrvJ32trhLCzV2_O7klreBBA3dUu&authuser=0&confirm=t', 'model.pth', quiet=False) |
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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) |
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import gzip |
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with gzip.open('data.txt.gz', 'rt', encoding='utf-8') as f: |
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dataset = f.read() |
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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', '🌴', '🌹', '🍌', '🙂'] |
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vocab_size = 212 |
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string_to_int = { ch:i for i,ch in enumerate(chars) } |
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int_to_string = { i:ch for i,ch in enumerate(chars) } |
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encode = lambda s: [string_to_int[c] for c in s] |
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decode = lambda l: ''.join([int_to_string[i] for i in l]) |
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data = torch.tensor(encode(dataset), dtype=torch.long) |
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class Head(nn.Module): |
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def __init__(self, head_size): |
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super().__init__() |
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self.key = nn.Linear(n_embd, head_size, bias=False) |
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self.query = nn.Linear(n_embd, head_size, bias=False) |
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self.value = nn.Linear(n_embd, head_size, bias=False) |
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self.register_buffer("tril", torch.tril(torch.ones(block_size, block_size))) |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, x): |
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B, T, C = x.shape |
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k = self.key(x) |
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q = self.query(x) |
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w = q @ k.transpose(-2, -1) * C**-0.5 |
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w = w.masked_fill(self.tril[:T, :T] == 0, float("-inf")) |
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w = F.softmax(w, dim=-1) |
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w = self.dropout(w) |
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v = self.value(x) |
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out = w @ v |
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return out |
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class MultiHeadAttention(nn.Module): |
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def __init__(self, num_heads, head_size): |
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super().__init__() |
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self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)]) |
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self.proj = nn.Linear(n_embd, n_embd) |
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self.dropout = nn.Dropout(dropout) |
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def forward(self, x): |
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out = torch.cat([h(x) for h in self.heads], dim=-1) |
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out = self.dropout(self.proj(out)) |
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return out |
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class FeedFoward(nn.Module): |
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def __init__(self, n_embd): |
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super().__init__() |
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self.net = nn.Sequential( |
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nn.Linear(n_embd, 4 * n_embd), |
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nn.ReLU(), |
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nn.Linear(4 * n_embd, n_embd), |
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nn.Dropout(dropout), |
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) |
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def forward(self, x): |
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return self.net(x) |
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class Block(nn.Module): |
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def __init__(self, n_embd, n_head): |
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super().__init__() |
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head_size = n_embd // n_head |
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self.sa = MultiHeadAttention(n_head, head_size) |
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self.ffwd = FeedFoward(n_embd) |
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self.ln1 = nn.LayerNorm(n_embd) |
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self.ln2 = nn.LayerNorm(n_embd) |
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def forward(self, x): |
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x = x + self.sa(self.ln1(x)) |
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x = x + self.ffwd(self.ln2(x)) |
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return x |
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class GPTLanguageModel(nn.Module): |
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def __init__(self, vocab_size): |
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super().__init__() |
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self.token_embedding_table = nn.Embedding(vocab_size, n_embd) |
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self.position_embedding_table = nn.Embedding(block_size, n_embd) |
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self.blocks = nn.Sequential( |
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*[Block(n_embd, n_head=n_head) for _ in range(n_layer)] |
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) |
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self.ln_f = nn.LayerNorm(n_embd) |
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self.lm_head = nn.Linear(n_embd, vocab_size) |
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def forward(self, idx, targets=None): |
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B, T = idx.shape |
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tok_emb = self.token_embedding_table(idx) |
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pos_emb = self.position_embedding_table(torch.arange(T, device=device)) |
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x = tok_emb + pos_emb |
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x = self.blocks(x) |
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x = self.ln_f(x) |
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logits = self.lm_head(x) |
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if targets is None: |
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loss = None |
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else: |
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B, T, C = logits.shape |
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logits = logits.view(B * T, C) |
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targets = targets.view(B * T) |
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loss = F.cross_entropy(logits, targets) |
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return logits, loss |
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def generate(self, idx, max_new_tokens): |
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for _ in range(max_new_tokens): |
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idx_cond = idx[:, -block_size:] |
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logits, loss = self(idx_cond) |
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logits = logits[:, -1, :] |
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probs = F.softmax(logits, dim=-1) |
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idx_next = torch.multinomial(probs, num_samples=1) |
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idx = torch.cat((idx, idx_next), dim=1) |
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return idx |
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@torch.no_grad() |
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def estimate_loss(): |
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out = {} |
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model.eval() |
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for split in ["training", "validation"]: |
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losses = torch.zeros(eval_iters) |
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for k in range(eval_iters): |
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X, Y = get_batch(split) |
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logits, loss = model(X, Y) |
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losses[k] = loss.item() |
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out[split] = losses.mean() |
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model.train() |
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return out |
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model = GPTLanguageModel(vocab_size) |
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m = model.to(device) |
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model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False) |
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model.eval() |
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def respond( |
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message, |
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max_tokens=512, |
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): |
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context = torch.tensor(encode(message), dtype=torch.long, device=device).unsqueeze( |
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0 |
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) |
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response = decode(model.generate(context, max_new_tokens=max_tokens)[0].tolist()) |
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return response |
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iface = gr.Interface( |
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fn=respond, |
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inputs=[ |
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gr.Textbox(lines=5, label="Message", value="Hi Harry Potter"), |
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gr.Slider(minimum=100, maximum=2048, value=256, label="Max Tokens"), |
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], |
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outputs="text", |
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title="PotterLLM", |
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description="A language model trained on Harry Potter Series.", |
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theme="huggingface", |
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) |
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if __name__ == "__main__": |
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iface.launch() |
