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import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import prepare_model_for_kbit_training
from peft import LoraConfig, get_peft_model
from datasets import load_dataset, DatasetDict
from config import config
from inference import get_bot_response
from rag import get_context
dataset = load_dataset("csv", data_files="tuning_data/tuning_dataset.csv")
train_test = dataset['train'].train_test_split(test_size=0.2)
dataset = DatasetDict({
'train': train_test['train'],
'valid': train_test['test']
})
model_name = "TheBloke/Mistral-7B-Instruct-v0.2-GPTQ"
model = AutoModelForCausalLM.from_pretrained(model_name,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
def tokenize_function(examples):
text = examples["sample"]
tokenizer.truncation_side = "left"
tokenized_inputs = tokenizer(
text,
return_tensors="np",
truncation=True,
max_length=512
)
return tokenized_inputs
tokenized_dataset = dataset.map(tokenize_function, batched=True)
if tokenizer.eos_token is None:
tokenizer.add_special_tokens({"eos_token": "</s>"})
tokenizer.pad_token = tokenizer.eos_token
data_collator = transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False)
model.train()
model.gradient_checkpointing_enable()
model = prepare_model_for_kbit_training(model)
lora_config = LoraConfig(
r=config["r"],
lora_alpha=config["lora_alpha"],
target_modules=config["target_modules"],
lora_dropout=config["lora_dropout"],
bias="none",
task_type="CAUSAL_LM"
)
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
lr = config["lr"]
batch_size = config["batch_size"]
num_epochs = config["num_epochs"]
training_args = transformers.TrainingArguments(
output_dir="MusicBot-ft",
learning_rate=lr,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
num_train_epochs=num_epochs,
weight_decay=0.01,
logging_strategy="epoch",
evaluation_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
gradient_accumulation_steps=4,
warmup_steps=2,
fp16=True,
optim="paged_adamw_8bit",
)
trainer = transformers.Trainer(
model=model,
train_dataset=tokenized_dataset["train"],
eval_dataset=tokenized_dataset["valid"],
args=training_args,
data_collator=data_collator
)
model.config.use_cache = False
if config["is_train"]:
trainer.train()
trainer.save_model("tuned_model")
if config["is_load_tuned"]:
model = AutoModelForCausalLM.from_pretrained(config["model_checkpoint"],
device_map="auto",
trust_remote_code=False,
revision="main")
request = config["request"]
context = get_context(request, config["top_k"])
response = get_bot_response(request, context, model, tokenizer)
print(response)
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