Create app.py

app.py

@@ -0,0 +1,121 @@
+import torch
+from datasets import Dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, BitsAndBytesConfig
+from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
+model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+
+tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+tokenizer.pad_token = tokenizer.eos_token
+model.config.use_cache = False
+model.gradient_checkpointing_enable()
+model = prepare_model_for_kbit_training(model)
+
+lora_config = LoraConfig(
+    r=8,
+    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)
+import pandas as pd
+from datasets import Dataset
+
+
+# Load data from CSV
+df = pd.read_csv("Customer-Support.csv")
+
+
+# Rename columns to match expected keys
+df = df.rename(columns={"query": "instruction", "response": "output"})
+
+
+# Select required columns
+data = df[["instruction", "output"]].fillna("")
+
+
+# Convert DataFrame to list of dictionaries
+data = data.to_dict(orient="records")
+
+
+# Create Hugging Face Dataset
+dataset = Dataset.from_list(data)
+
+
+# Format each example
+def format_instruction(example):
+    return f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['output']}"
+
+
+# Map formatted text
+dataset = dataset.map(lambda x: {"text": format_instruction(x)})
+
+def tokenize_function(example):
+    tokenized = tokenizer(example["text"], truncation=True, padding="max_length", max_length=512)
+    tokenized["labels"] = tokenized["input_ids"].copy()
+    return tokenized
+
+tokenized_dataset = dataset.map(tokenize_function, batched=True)
+training_args = TrainingArguments(
+    output_dir="./tinyllama-qlora-support-bot",
+    per_device_train_batch_size=2,
+    gradient_accumulation_steps=4,
+    learning_rate=2e-4,
+    logging_dir="./logs",
+    num_train_epochs=3,
+    logging_steps=10,
+    save_total_limit=2,
+    save_strategy="epoch",
+    bf16=True,
+    optim="paged_adamw_8bit"
+)
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_dataset,
+    tokenizer=tokenizer
+)
+
+trainer.train()
+model.save_pretrained("tinyllama-qlora-support-bot")
+tokenizer.save_pretrained("tinyllama-qlora-support-bot")
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+instruction = "How do I update the app?"
+prompt = f"### Instruction:\n{instruction}\n\n### Response:\n"
+
+output = pipe(prompt, max_new_tokens=100)
+print(output[0]['generated_text'])
+import gradio as gr
+
+def generate_response(instruction):
+    prompt = f"### Instruction:\n{instruction}\n\n### Response:\n"
+    output = pipe(prompt, max_new_tokens=100, do_sample=True, temperature=0.7)
+    # Extract only the response part
+    response = output[0]["generated_text"].split("### Response:\n")[-1].strip()
+    return response
+
+gr.Interface(
+    fn=generate_response,
+    inputs=gr.Textbox(lines=3, label="Enter your question"),
+    outputs=gr.Textbox(lines=5, label="Support Bot's Response"),
+    title="📞 Customer Support Chatbot",
+    description="Ask a question and get a response from your fine-tuned TinyLLaMA model.",
+).launch()