s12

Character-level GPT Model

This is a custom character-level GPT model trained on a text dataset (e.g., Shakespeare). It's a minimal implementation designed for educational purposes.

Model Architecture

The model is a Transformer-based decoder-only architecture, similar to GPT-2, but operating at the character level.

• block_size: 1024
• vocab_size: Dynamically determined from training data
• n_layer: 12
• n_head: 12
• n_embd: 768

How to Use

To use this model, you'll need the pytorch_model.bin (weights) and vocab.json (character mappings).

import torch
import json
from dataclasses import dataclass
import torch.nn as nn
from torch.nn import functional as F
import math

# --- Define your model classes (GPTConfig, CausalSelfAttention, MLP, Block, GPT) here ---
# Copy the relevant classes from your training script.

@dataclass
class GPTConfig:
    block_size: int = 1024
    vocab_size: int = 50257
    n_layer: int = 12
    n_head: int = 12
    n_embd: int = 768

# ... (CausalSelfAttention, MLP, Block, GPT class definitions) ...

class CausalSelfAttention(nn.Module):
    '''A minimal Causal Self-Attention block.'''
    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.NANGPT_SCALE_INIT = 1.0 / math.sqrt(2.0 * config.n_layer)
        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()
        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)
        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):
    '''A minimal Multi-Layer Perceptron block.'''
    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.NANGPT_SCALE_INIT = 1.0 / math.sqrt(2.0 * config.n_layer)

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        return x

class Block(nn.Module):
    '''A minimal Transformer Block consisting of Attention and MLP.'''
    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

class GPT(nn.Module):
    '''The full GPT model composed of Blocks.'''
    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 # Weight tying
        self.apply(self._init_weights)

    def get_num_params(self, non_embedding=True):
        n_params = sum(p.numel() for p in self.parameters())
        if non_embedding:
            n_params -= self.transformer.wpe.weight.numel()
        return n_params

    def _init_weights(self, module):
        if isinstance(module, nn.Linear):
            std = 0.02
            if hasattr(module, 'NANGPT_SCALE_INIT'):
                std *= module.NANGPT_SCALE_INIT
            torch.nn.init.normal_(module.weight, mean=0.0, std=std)
            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)
        elif isinstance(module, nn.LayerNorm):
            torch.nn.init.zeros_(module.bias)
            torch.nn.init.ones_(module.weight)

    def forward(self, idx, targets=None):
        device = idx.device
        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=device).unsqueeze(0)

        tok_emb = self.transformer.wte(idx)
        pos_emb = self.transformer.wpe(pos)
        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), ignore_index=-1)

        return logits, loss


# --- Custom tokenizer based on vocab.json ---
class SimpleCharTokenizer:
    def __init__(self, vocab_file):
        with open(vocab_file, 'r') as f:
            vocab_data = json.load(f)
        self.stoi = vocab_data['stoi']
        self.itos = {int(k): v for k, v in vocab_data['itos'].items()} # keys are string in json
        self.vocab_size = vocab_data['vocab_size']

    def encode(self, s):
        return [self.stoi[c] for c in s]

    def decode(self, l):
        return ''.join([self.itos[i] for i in l])


# --- Generation function (simplified) ---
def generate_from_hf(model, tokenizer, start_str, max_new_tokens, temperature=1.0, top_k=50, device='cpu'):
    model.eval()
    B, T_model = 1, model.config.block_size # Model's block_size

    start_ids = tokenizer.encode(start_str)
    x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])

    x = x[:, -T_model:] # Truncate if start string is too long for model's block_size

    for _ in range(max_new_tokens):
        # crop context if necessary
        x_cond = x if x.size(1) <= T_model else x[:, -T_model:]

        with torch.no_grad():
            logits, _ = model(x_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)

        x = torch.cat((x, idx_next), dim=1)

        if tokenizer.stoi.get('
') is not None and idx_next.item() == tokenizer.stoi.get('
'):
             break

    return tokenizer.decode(x[0].tolist())



# Example usage:
# device = 'cuda' if torch.cuda.is_available() else 'cpu'
# # Load config and vocab
# with open('my_gpt_model/config.json', 'r') as f:
#     model_config_dict = json.load(f)
# model_config = GPTConfig(**model_config_dict)
#
# tokenizer = SimpleCharTokenizer('my_gpt_model/vocab.json')
# model = GPT(model_config).to(device)
# model.load_state_dict(torch.load('my_gpt_model/pytorch_model.bin', map_location=device))
#
# prompt = "First Citizen:"
# generated_text = generate_from_hf(model, tokenizer, prompt, max_new_tokens=200, temperature=0.9, device=device)
# print(generated_text)

Files in . directory:

• pytorch_model.bin: Contains the model's state dictionary (weights).
• vocab.json: Contains the character-to-integer (stoi) and integer-to-character (itos) mappings.
• config.json: Contains the model's configuration parameters (GPTConfig).

How to Load and Generate Text

# (Refer to the example usage in the code block above for loading and generating text)

Note: The model architecture classes (GPTConfig, CausalSelfAttention, MLP, Block, GPT) and the generate function itself are part of the model's definition and would need to be present in your environment when loading the model from Hugging Face. The README.md includes these definitions for clarity and ease of use.