Create app.py

app.py

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+from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
+import torch
+
+# Load pre-trained model and tokenizer
+model_name = "facebook/llama-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Load and preprocess your chat data
+# This is a simplified example; you'll need to adapt it to your data format
+train_data = ["Hello, how are you?", "I'm fine, thank you."]
+train_encodings = tokenizer(train_data, padding=True, truncation=True, return_tensors="pt")
+
+# Define a custom dataset
+class ChatDataset(torch.utils.data.Dataset):
+    def __init__(self, encodings):
+        self.encodings = encodings
+
+    def __len__(self):
+        return len(self.encodings["input_ids"])
+
+    def __getitem__(self, idx):
+        return {key: val[idx] for key, val in self.encodings.items()}
+
+train_dataset = ChatDataset(train_encodings)
+
+# Define training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    logging_dir="./logs",
+)
+
+# Initialize the Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+)
+
+# Start training
+trainer.train()