Create app.py

app.py

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+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
+from peft import prepare_model_for_kbit_training, LoraConfig, get_peft_model
+from trl import SFTTrainer
+import torch
+from datasets import load_dataset
+
+# Load the base model (TinyLlama)
+model_name = "NousResearch/Hermes-3-Llama-3.2-3B"
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", load_in_4bit=True)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+# Prepare model for QLoRA
+model = prepare_model_for_kbit_training(model)
+
+# LoRA Configuration
+lora_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+model = get_peft_model(model, lora_config)
+model.print_trainable_parameters()
+
+# Load dataset
+dataset = load_dataset("json", data_files="sevaai_faq.json")
+from datasets import load_dataset
+
+# Load dataset from your JSON file
+dataset = load_dataset("json", data_files="sevaai_faq.json")
+
+# Rename the "output" column to "text" so SFTTrainer can find it
+dataset["train"] = dataset["train"].rename_column("output", "text")
+
+# Training arguments
+training_args = TrainingArguments(
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=8,
+    num_train_epochs=3,
+    learning_rate=2e-4,
+    logging_steps=10,
+    output_dir="./nirmaya",
+    save_steps=1000,
+    save_total_limit=2,
+    optim="adamw_torch"
+)
+
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=dataset["train"],
+    peft_config=lora_config,
+
+    tokenizer=tokenizer,
+    args=training_args
+)
+
+
+
+# Train the model
+trainer.train()
+
+# Save fine-tuned model
+trainer.save_model("./nirmaya")
+print("Fine-tuning complete! Model saved to ./nirmaya")