File size: 2,378 Bytes
3015143 | 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 | # pip install torch peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 accelerate einops
# pip install tqdm scipy
from transformers import pipeline
import torch
from datasets import load_dataset
from datasets import load_from_disk
from peft import LoraConfig, prepare_model_for_kbit_training
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
TrainingArguments,
)
from trl import SFTTrainer
training_dataset = load_dataset("csv", data_files="formatted_qna_lite.csv", split="train")
print(training_dataset)
print("Is CUDA enabled?",torch.cuda.is_available())
base_model = "microsoft/phi-2"
new_model = "phi-2-qna"
tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
tokenizer.pad_token=tokenizer.eos_token
tokenizer.padding_side="right"
model = AutoModelForCausalLM.from_pretrained(
base_model,
#quantization_config=bnb_config,
# use_flash_attention_2=True, # Phi does not support yet.
trust_remote_code=True,
flash_attn=True,
flash_rotary=True,
fused_dense=True,
revision="refs/pr/23",
)
model.config.use_cache = False
model.config.pretraining_tp = 1
training_arguments = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
per_device_train_batch_size=2,
gradient_accumulation_steps=32,
evaluation_strategy="steps",
eval_steps=2000,
logging_steps=15,
optim="adamw_hf",
learning_rate=2e-4,
lr_scheduler_type="cosine",
save_steps=2000,
warmup_ratio=0.05,
weight_decay=0.01,
max_steps=-1
)
peft_config = LoraConfig(
r=32,
lora_alpha=64,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules= ["Wqkv", "fc1", "fc2" ] # ["Wqkv", "out_proj", "fc1", "fc2" ], - 41M params
# modules_to_save=["embed_tokens","lm_head"]
)
trainer = SFTTrainer(
model=model,
train_dataset=training_dataset,
peft_config= peft_config,
dataset_text_field="Text",
max_seq_length=690,
tokenizer=tokenizer,
args=training_arguments,
)
trainer.train()
prompt = "How old was Pascal when he lost his mother?"
pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=250)
while prompt != 'EXIT':
result = pipe(f"[INST] {prompt} [/INST]")
print(result[0]['generated_text'])
print("\n")
prompt = input("Ask the next question .....\n") |