Create train.py

train.py

@@ -0,0 +1,128 @@
+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 = 128
+BATCH_SIZE = 256
+EPOCHS = 8
+LEARNING_RATE = 1e-4
+FP16 = True
+FACTOR = 2
+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):
+    config = LlamaConfig(
+        vocab_size=tokenizer.vocab_size,
+        hidden_size=FACTOR,
+        intermediate_size=FACTOR * 2,
+        num_hidden_layers=max(1, FACTOR // 64),
+        num_attention_heads=max(1, FACTOR // 64),
+        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)
+    train_model(model, tokenizer, dataset)
+
+if __name__ == "__main__":
+    main()
+    raise RuntimeError("The script is finished.")