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import os | |
import torch | |
import trl | |
from transformers import AutoTokenizer, LlamaConfig, LlamaForCausalLM, TrainingArguments, PreTrainedTokenizerFast | |
from datasets import load_dataset | |
from tokenizers import ByteLevelBPETokenizer | |
MAX_SEQ_LENGTH = 512 | |
BATCH_SIZE = 256 | |
EPOCHS = 3 | |
LEARNING_RATE = 1e-4 | |
FP16 = True | |
FACTOR = 8 | |
VOCAB_SIZE = 3200 | |
INPUT_DATASET = "nroggendorff/elephant" | |
OUTPUT_REPO = "smallama" | |
def load_data(): | |
dataset = load_dataset(INPUT_DATASET, split="train") | |
return dataset | |
def create_tokenizer(training_corpus): | |
tokenizer = ByteLevelBPETokenizer() | |
tokenizer.train_from_iterator( | |
training_corpus, | |
vocab_size=VOCAB_SIZE, | |
min_frequency=2, | |
special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>", "<|user|>", "<|bot|>", "<|end|>"] | |
) | |
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer._tokenizer) | |
return fast_tokenizer | |
def get_training_corpus(dataset): | |
for i in range(0, len(dataset), 1000): | |
yield dataset[i : i + 1000]["text"] | |
def format_prompts(examples, tokenizer): | |
texts = [] | |
for text in examples['text']: | |
conversation = [] | |
parts = text.split('<|end|>') | |
for i in range(0, len(parts) - 1, 2): | |
prompt = parts[i].replace("<|user|>", "") | |
response = parts[i + 1].replace("<|bot|>", "") | |
conversation.append({"role": "user", "content": prompt}) | |
conversation.append({"role": "assistant", "content": response}) | |
formatted_conversation = tokenizer.apply_chat_template(conversation, tokenize=False) | |
texts.append(formatted_conversation) | |
return {"text": texts} | |
def create_model(tokenizer, factor): | |
config = LlamaConfig( | |
vocab_size=tokenizer.vocab_size, | |
hidden_size=512 // factor, | |
intermediate_size=1024 // factor, | |
num_hidden_layers=8 // factor, | |
num_attention_heads=8 // factor, | |
max_position_embeddings=MAX_SEQ_LENGTH, | |
rms_norm_eps=1e-6, | |
initializer_range=0.02, | |
use_cache=True, | |
pad_token_id=tokenizer.pad_token_id, | |
bos_token_id=tokenizer.bos_token_id, | |
eos_token_id=tokenizer.eos_token_id, | |
tie_word_embeddings=False, | |
) | |
model = LlamaForCausalLM(config) | |
return model | |
def configure_tokenizer(tokenizer): | |
special_tokens = { | |
"bos_token": "<s>", | |
"eos_token": "</s>", | |
"unk_token": "<unk>", | |
"pad_token": "<pad>", | |
"mask_token": "<mask>", | |
"additional_special_tokens": ["<|user|>", "<|bot|>", "<|end|>"] | |
} | |
tokenizer.add_special_tokens(special_tokens) | |
tokenizer.user_token_id = tokenizer.convert_tokens_to_ids("<|user|>") | |
tokenizer.assistant_token_id = tokenizer.convert_tokens_to_ids("<|bot|>") | |
chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '<|user|>\n' + message['content'] + '<|end|>\n' }}{% elif message['role'] == 'assistant' %}{{ '<|bot|>\n' + message['content'] + '<|end|>\n' }}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}{{ eos_token }}" | |
tokenizer.chat_template = chat_template | |
def train_model(model, tokenizer, dataset): | |
args = TrainingArguments( | |
output_dir="model", | |
num_train_epochs=EPOCHS, | |
per_device_train_batch_size=BATCH_SIZE, | |
learning_rate=LEARNING_RATE, | |
fp16=FP16, | |
optim="sgd" | |
) | |
dataset = dataset.map(lambda examples: format_prompts(examples, tokenizer), batched=True) | |
trainer = trl.SFTTrainer( | |
model=model, | |
tokenizer=tokenizer, | |
args=args, | |
train_dataset=dataset, | |
dataset_text_field='text', | |
max_seq_length=MAX_SEQ_LENGTH | |
) | |
trainer.train() | |
trained_model = trainer.model | |
trained_tokenizer = trainer.tokenizer | |
repo_id = OUTPUT_REPO | |
trained_model.push_to_hub(repo_id) | |
trained_tokenizer.push_to_hub(repo_id) | |
def main(): | |
dataset = load_data() | |
training_corpus = get_training_corpus(dataset) | |
tokenizer = create_tokenizer(training_corpus) | |
configure_tokenizer(tokenizer) | |
model = create_model(tokenizer, FACTOR) | |
train_model(model, tokenizer, dataset) | |
if __name__ == "__main__": | |
main() |