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---
base_model: GPT-2-like-character-level
tags:
- text-generation
- character-level
- pytorch
---
# 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).
```python
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
```python
# (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.
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