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from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import Chroma
import time
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, TrainingArguments, DataCollatorForLanguageModeling
from trl import SFTTrainer
from peft import get_peft_model, LoraConfig, prepare_model_for_kbit_training
# create the open-source embedding function
embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
# load Chroma
db = Chroma(embedding_function=embedding_function, persist_directory="./chroma_db")
print("There are", db._collection.count(), " docs in the collection")
docs = db._collection.peek(db._collection.count())
dataset = docs['documents']
if torch.cuda.is_available():
print("Cuda is available")
base_model_id = "microsoft/phi-2"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
print("pad_token was missing and has been set to eos_token")
# Configuration to load model in 4-bit quantized
bnb_config = BitsAndBytesConfig(load_in_4bit=True,
bnb_4bit_quant_type='nf4',
#bnb_4bit_compute_dtype='float16',
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=False)
model = AutoModelForCausalLM.from_pretrained(base_model_id, attn_implementation="flash_attention_2", quantization_config=bnb_config, torch_dtype="auto")
print(model)
# Gradient checkpointing to save memory
model.gradient_checkpointing_enable()
# Freeze base model layers and cast layernorm in fp32
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=True)
peft_config = LoraConfig(
r=64,
lora_alpha=64,
target_modules= ["q_proj","k_proj","v_proj","dense","fc2","fc1"],
bias="none",
lora_dropout=0.05,
task_type="CAUSAL_LM",
)
training_args = TrainingArguments(
output_dir='./results', # Output directory for checkpoints and predictions
overwrite_output_dir=True, # Overwrite the content of the output directory
per_device_train_batch_size=2, # Batch size for training
per_device_eval_batch_size=2, # Batch size for evaluation
gradient_accumulation_steps=5, # number of steps before optimizing
gradient_checkpointing=True, # Enable gradient checkpointing
gradient_checkpointing_kwargs={"use_reentrant": False},
warmup_steps=10, # Number of warmup steps
#max_steps=1000, # Total number of training steps
num_train_epochs=20, # Number of training epochs
learning_rate=5e-5, # Learning rate
weight_decay=0.01, # Weight decay
optim="paged_adamw_8bit", #Keep the optimizer state and quantize it
bf16=True, #Use mixed precision training
#For logging and saving
logging_dir='./logs',
logging_strategy="epoch",
logging_steps=10,
save_strategy="epoch",
save_steps=10,
save_total_limit=2, # Limit the total number of checkpoints
evaluation_strategy="epoch",
eval_steps=10,
load_best_model_at_end=True, # Load the best model at the end of training
lr_scheduler_type="linear",
)
def formatting_func(doc):
return doc
trainer = SFTTrainer(
model=model,
train_dataset=dataset,
eval_dataset=dataset,
peft_config=peft_config,
args=training_args,
max_seq_length=1024,
packing=True,
formatting_func=formatting_func
)
model.config.use_cache = False # silence the warnings. Please re-enable for inference!
start_time = time.time() # Record the start time
trainer.train()
end_time = time.time() # Record the end time
training_time = end_time - start_time # Calculate total training time
trainer.save_model("./results")
print(f"Training completed in {training_time} seconds.") |