JamaicanTinyStories

Model Description

This is a Transformer-based causal language model trained on the Jamaican Tiny Stories dataset. The model architecture is a decoder-only transformer with positional encoding.

• Model type: Causal Language Model
• Architecture: Transformer Decoder
• Tokenizer: Based on the tokenizer from "roneneldan/TinyStories-33M"
• Vocabulary Size: 50257

Training Code

https://colab.research.google.com/drive/1BcWKK3jBNvJk-uDpG3zFyN3HWsLP8nPo?usp=sharing

Training Data

The model was trained on the "Jaia-Admin/JamaicanTinyStories_400K_sample" dataset, which contains Jamaican Patois stories.

Training Procedure

• Epochs: 20 (though training was interrupted)
• Batch Size: 16
• Optimizer: AdamW with a learning rate of 5e-5
• Loss Function: Cross-Entropy Loss
• Mixed Precision Training: Enabled using torch.amp.autocast and GradScaler.
• Learning Rate Scheduler: ReduceLROnPlateau

Model Parameters

• Embedding Size: 768
• Number of Layers: 3
• Number of Heads: 8
• Feed-Forward Dimension: 768 * 4 = 3072
• Maximum Sequence Length: 512

Usage

The model can be used to generate text based on a given prompt. The generate method in the TransformerGenerator class handles the text generation with options for controlling the output (temperature, top-k, top-p).

from transformers import AutoTokenizer
import math
import torch
import os
from torch import nn
import torch.nn.functional as F
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM

device = "cuda" if torch.cuda.is_available() else "cpu"

# Load the tokenizer
model_name = "JAMAICA-AI/JamaicanTinyStories"
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define model parameters (should match the trained model)
vocab_size = tokenizer.vocab_size
embed_size = 768
num_layers = 3
num_heads = 8
ff_dim = 768 * 4
max_len = 512

class PositionalEncoding(nn.Module):
    def __init__(self, d_model=768, dropout=0.1, max_len=1024):
        super(PositionalEncoding, self).__init__()
        self.dropout = nn.Dropout(p=dropout)

        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        self.register_buffer('pe', pe)

    def forward(self, x):
        x = x + self.pe[:x.size(1), :].unsqueeze(0)
        return self.dropout(x)

class TransformerGenerator(nn.Module):
    def __init__(self, vocab_size, embed_size, num_layers, num_heads, ff_dim, max_len=512):
        super(TransformerGenerator, self).__init__()
        self.token_embedding = nn.Embedding(vocab_size, embed_size)
        self.pos_encoding = PositionalEncoding(d_model=embed_size, max_len=max_len)

        decoder_layer = nn.TransformerDecoderLayer(
            d_model=embed_size,
            nhead=num_heads,
            dim_feedforward=ff_dim,
            batch_first=True,
            activation=F.gelu,
            bias=False
        )
        self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=num_layers)

        self.fc_out = nn.Linear(embed_size, vocab_size)
        self.max_len = max_len

    def forward(self, inputs, tgt_mask=None, memory_mask=None, tgt_key_padding_mask=None, memory_key_padding_mask=None):
        inputs = self.token_embedding(inputs)
        inputs = self.pos_encoding(inputs)
        # In a decoder-only model, the target and memory are the same.
        causal_mask = nn.Transformer.generate_square_subsequent_mask(inputs.size(1)).to(device)
        output = self.transformer_decoder(inputs, inputs, tgt_mask=causal_mask, memory_mask=causal_mask)
        output = self.fc_out(output)
        return output

    def generate(self, prompt_ids, max_length=100, temperature=1.0, top_k=50, top_p=0.95, eos_token_id=None):
        self.eval()
        with torch.no_grad():
            input_ids = prompt_ids.to(self.fc_out.weight.device)

            for _ in range(max_length):
                # Create a causal mask for the decoder
                seq_len = input_ids.size(1)
                causal_mask = torch.triu(torch.ones(seq_len, seq_len, device=input_ids.device), diagonal=1).bool()
                causal_mask = causal_mask.masked_fill(causal_mask == 1, float('-inf')).masked_fill(causal_mask == 0, float(0.0))

                # Get predictions
                output = self.forward(input_ids, tgt_mask=causal_mask)

                # Get the logits for the last token
                logits = output[:, -1, :] / temperature

                # Apply top-k and top-p sampling
                if top_k is not None:
                    v, _ = torch.topk(logits, top_k)
                    logits[logits < v[:, [-1]]] = -float('Inf')
                if top_p is not None:
                    sorted_logits, sorted_indices = torch.sort(logits, dim=-1, descending=True)
                    cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)

                    # Remove tokens with cumulative probability above the threshold
                    sorted_indices_to_remove = cumulative_probs > top_p
                    # Shift the indices to the right to keep the first token above the threshold
                    sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
                    sorted_indices_to_remove[..., 0] = 0

                    indices_to_remove = sorted_indices[sorted_indices_to_remove]
                    logits[:, indices_to_remove] = -float('Inf')

                # Sample the next token
                probs = F.softmax(logits, dim=-1)
                next_token = torch.multinomial(probs, num_samples=1)

                # Append the next token to the input sequence
                input_ids = torch.cat([input_ids, next_token], dim=1)

                # Stop if the end-of-sequence token is generated or max length is reached
                if eos_token_id is not None and next_token.item() == eos_token_id:
                    break

        return input_ids


# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.bos_token = tokenizer.bos_token
tokenizer.eos_token = tokenizer.eos_token
tokenizer.pad_token = tokenizer.eos_token
vocab_size = tokenizer.vocab_size

vocab_size = tokenizer.vocab_size

model = TransformerGenerator(
    vocab_size=vocab_size,
    embed_size=768,
    num_layers=3,
    num_heads=8,
    ff_dim=768*4,
    max_len=512
    )

model.to(device)

model_path = hf_hub_download(repo_id=model_name, filename="pytorch_model.bin")
model.load_state_dict(torch.load(model_path, map_location=device))
model.to(device)


def text(prompt):
    model.eval()
    with torch.no_grad():
        input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)

        # Generate text
        output = model.generate(input_ids, max_length=100, temperature=0.7, eos_token_id=tokenizer.eos_token_id)

        # Decode and print the generated text
        generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
        print(generated_text)

# Example usage
text("Once upon a time")