Create train.py

train.py

@@ -0,0 +1,34 @@
+from unsloth import FastLanguageModel
+from datasets import load_dataset
+import torch
+
+# Load your chat data as JSONL
+dataset = load_dataset("json", data_files="your_chats.jsonl", split="train")
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "unsloth/llama-3.1-8b-bnb-4bit",
+    max_seq_length=2048,
+    dtype=torch.float16
+)
+
+# Add LoRA adapters (fine-tune 1% of params)
+model = FastLanguageModel.get_peft_model(
+    model, r=16, target_modules=["q_proj", "k_proj", "v_proj", "o_proj"]
+)
+
+# Train (2-4 hours on T4)
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset,
+    dataset_text_field="text",
+    max_seq_length=2048,
+    args=TrainingArguments(
+        per_device_train_batch_size=2,
+        gradient_accumulation_steps=4,
+        warmup_steps=10,
+        max_steps=100,  # Your data size
+        output_dir="fine_tuned_model"
+    )
+)
+trainer.train()
+model.save_pretrained("your_finance_bot")