File size: 3,506 Bytes
917310d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | 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()
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