Update app.py

app.py

@@ -1,27 +1,88 @@
+import torch
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    TrainingArguments,
+    Trainer,
+    DataCollatorForLanguageModeling
+)
+from datasets import load_dataset
 import os
-from autotrain import logger
-from autotrain.trainers.common import ALLOW_REMOTE_CODE
-from autotrain.trainers.text_generation import LLMTrainingParams, LLMTrainer
 
 def train():
-    
-
-    # Define training parameters
-    params = LLMTrainingParams(
-        model_name="microsoft/phi-4",  # Replace with your model
-        data_path="lavita/medical-qa-datasets",
-        project_name="phi4-training",
-        learning_rate=2e-5,
+    # Load model and tokenizer
+    model_name = "microsoft/phi-2"
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cpu", trust_remote_code=True)
+
+    # Add padding token if missing
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    # Load dataset (update paths as needed)
+    dataset = load_dataset(
+        "csv",
+        data_files={
+            "train": "eswardivi/medical_qa",
+            "validation": "eswardivi/medical_qa"
+        }
+    )
+
+    # Tokenization function
+    def tokenize_function(examples):
+        return tokenizer(
+            examples["text"],
+            padding="max_length",
+            truncation=True,
+            max_length=256,
+            return_tensors="pt",
+        )
+
+    # Preprocess dataset
+    tokenized_dataset = dataset.map(
+        tokenize_function,
+        batched=True,
+        remove_columns=["text"]
+    )
+
+    # Data collator
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer,
+        mlm=False
+    )
+
+    # Training arguments
+    training_args = TrainingArguments(
+        output_dir="./phi2-cpu-results",
+        overwrite_output_dir=True,
+        per_device_train_batch_size=2,
+        per_device_eval_batch_size=2,
         num_train_epochs=3,
-        batch_size=2,
-        fp16=True,
-        push_to_hub=True,
-        repo_id="hackergeek98/phi4-trained",
+        logging_dir="./logs",
+        logging_steps=100,
+        evaluation_strategy="epoch",
+        save_strategy="epoch",
+        fp16=False,
+        report_to="none",
     )
 
-    # Initialize and run trainer
-    trainer = LLMTrainer(params=params)
+    # Initialize Trainer
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_dataset["train"],
+        eval_dataset=tokenized_dataset["validation"],
+        data_collator=data_collator,
+    )
+
+    # Start training
+    print("Starting training...")
     trainer.train()
 
+    # Save model
+    trainer.save_model("./phi2-trained-model")
+    tokenizer.save_pretrained("./phi2-trained-model")
+    print("Training complete! Model saved.")
+
 if __name__ == "__main__":
     train()