Update train.py
Browse files
train.py
CHANGED
@@ -4,25 +4,29 @@ from datasets import load_dataset
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import os
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# Model and tokenizer setup
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MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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OUTPUT_DIR = "./mixtral_finetuned"
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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tokenizer.pad_token
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# Load model with
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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low_cpu_mem_usage=True
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)
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# Load dataset (local
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dataset = load_dataset("text", data_files={"train": "train.txt", "validation": "val.txt"})
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# Tokenize dataset
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def tokenize_function(examples):
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@@ -30,41 +34,44 @@ def tokenize_function(examples):
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examples["text"],
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padding="max_length",
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truncation=True,
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max_length=512,
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return_tensors="pt"
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)
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tokenized["labels"] = tokenized["input_ids"].clone()
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return tokenized
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tokenized_datasets = dataset.map(
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tokenize_function,
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batched=True,
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remove_columns=["text"]
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)
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# Split dataset
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train_dataset = tokenized_datasets["train"]
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eval_dataset = tokenized_datasets["validation"]
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# Define training arguments
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training_args = TrainingArguments(
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output_dir=OUTPUT_DIR,
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evaluation_strategy="epoch",
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per_device_train_batch_size=
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per_device_eval_batch_size=
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num_train_epochs=3,
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learning_rate=2e-5,
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weight_decay=0.01,
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gradient_accumulation_steps=
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bf16=True,
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fp16=False,
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save_strategy="epoch",
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save_total_limit=2,
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logging_dir="./logs",
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logging_steps=10,
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load_best_model_at_end=
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metric_for_best_model="loss",
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report_to="none"
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)
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# Initialize Trainer
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@@ -76,13 +83,17 @@ trainer = Trainer(
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)
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# Train the model
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# Save locally
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trainer.save_model(OUTPUT_DIR)
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tokenizer.save_pretrained(OUTPUT_DIR)
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# Clean up
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del model
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torch.cuda.empty_cache()
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print(f"Model and tokenizer saved to {OUTPUT_DIR}")
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import os
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# Model and tokenizer setup
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MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"
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OUTPUT_DIR = "./mixtral_finetuned"
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token # Fallback if undefined
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tokenizer.pad_token_id = tokenizer.eos_token_id
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# Load model with optimizations
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model = AutoModelForCausalLM.from_pretrained(
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MODEL_NAME,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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low_cpu_mem_usage=True
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)
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# Load dataset (local text files)
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try:
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dataset = load_dataset("text", data_files={"train": "train.txt", "validation": "val.txt"})
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except FileNotFoundError:
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print("Error: train.txt or val.txt not found. Please provide valid files.")
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exit(1)
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# Tokenize dataset
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def tokenize_function(examples):
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examples["text"],
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padding="max_length",
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truncation=True,
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max_length=512, # Adjust to 2048 or 4096 if needed
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return_tensors="pt"
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)
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tokenized["labels"] = tokenized["input_ids"].clone()
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return tokenized
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tokenized_datasets = dataset.map(
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tokenize_function,
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batched=True,
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remove_columns=["text"]
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)
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# Split dataset with validation check
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train_dataset = tokenized_datasets["train"]
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eval_dataset = tokenized_datasets["validation"] if "validation" in tokenized_datasets else None
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if not train_dataset or (eval_dataset and len(eval_dataset) == 0):
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print("Error: Empty training or validation dataset.")
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exit(1)
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# Define training arguments
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training_args = TrainingArguments(
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output_dir=OUTPUT_DIR,
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evaluation_strategy="epoch" if eval_dataset else "no", # Skip eval if no validation
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per_device_train_batch_size=1, # Lowered for smaller GPUs; adjust up if possible
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per_device_eval_batch_size=1,
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num_train_epochs=3,
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learning_rate=2e-5,
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weight_decay=0.01,
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gradient_accumulation_steps=8, # Effective batch size = 8
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bf16=True,
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fp16=False,
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save_strategy="epoch",
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save_total_limit=2,
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logging_dir="./logs",
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logging_steps=10,
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load_best_model_at_end=bool(eval_dataset), # Only if eval exists
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metric_for_best_model="loss",
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report_to="none"
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)
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# Initialize Trainer
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)
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# Train the model
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try:
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trainer.train()
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except RuntimeError as e:
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print(f"Training failed: {e} (Likely OOM—reduce batch size or max_length)")
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exit(1)
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# Save locally
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trainer.save_model(OUTPUT_DIR)
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tokenizer.save_pretrained(OUTPUT_DIR)
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# Clean up
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del model
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torch.cuda.empty_cache()
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print(f"Model and tokenizer saved to {OUTPUT_DIR}")
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