| 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 |
|
|
|
|
| |
| df = pd.read_csv("Customer-Support.csv") |
|
|
|
|
| |
| df = df.rename(columns={"query": "instruction", "response": "output"}) |
|
|
|
|
| |
| data = df[["instruction", "output"]].fillna("") |
|
|
|
|
| |
| data = data.to_dict(orient="records") |
|
|
|
|
| |
| dataset = Dataset.from_list(data) |
|
|
|
|
| |
| def format_instruction(example): |
| return f"### Instruction:\n{example['instruction']}\n\n### Response:\n{example['output']}" |
|
|
|
|
| |
| 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) |
| |
| 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() |
|
|
