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# %%
from datasets import Dataset, load_dataset
import pandas as pd
from transformers import AutoTokenizer, AutoModelForCausalLM, DataCollatorForSeq2Seq, TrainingArguments, Trainer
from peft import LoraConfig, TaskType, get_peft_model, AutoPeftModelForCausalLM
# %%
df = pd.read_csv('data/riddles_data.csv')
df =df.sample(frac = 1)
#df = df[:1000]
# %%
df.describe()
# %%
ds = Dataset.from_pandas(df)
# %%
ds[:3]
# %%
llm_model_name="Qwen/Qwen1.5-0.5B-Chat"
model = AutoModelForCausalLM.from_pretrained(llm_model_name)
tokenizer = AutoTokenizer.from_pretrained(llm_model_name,trust_remote_code=True, pad_token='<|endoftext|>')
tokenizer
# %%
def process_func(example):
MAX_LENGTH = 512
input_ids, attention_mask, labels = [], [], []
instruction = tokenizer(f"<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\n猜谜语：\n谜面：{example['riddle']}\n\n谜底是什么？<|im_end|>\n<|im_start|>assistant\n", add_special_tokens=False) # add_special_tokens 不在开头加 special_tokens
response = tokenizer(f"谜底是：{example['label']}", add_special_tokens=False)
input_ids = instruction["input_ids"] + response["input_ids"] + [tokenizer.pad_token_id]
attention_mask = instruction["attention_mask"] + response["attention_mask"] + [1]
labels = [-100] * len(instruction["input_ids"]) + response["input_ids"] + [tokenizer.pad_token_id]
if len(input_ids) > MAX_LENGTH: # 做一个截断
input_ids = input_ids[:MAX_LENGTH]
attention_mask = attention_mask[:MAX_LENGTH]
labels = labels[:MAX_LENGTH]
print (f"{tokenizer.decode(input_ids)} Too Long")
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels
}
# %%
tokenized_id = ds.map(process_func, remove_columns=ds.column_names)
tokenized_id
# %%
tokenizer.decode(tokenized_id[0]['input_ids'])
# %%
tokenizer.decode(list(filter(lambda x: x != -100, tokenized_id[1]["labels"])))
# %%
config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
#target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
inference_mode=False,
r=32,
lora_alpha=32,
lora_dropout=0.05
)
# %%
model = get_peft_model(model, config)
config
# %%
model.print_trainable_parameters()
# %%
args = TrainingArguments(
output_dir="./Qwen1.5_0.5B_Chat_sft_full",
logging_steps=10,
num_train_epochs=2,
save_steps=10,
learning_rate=1e-4,
save_on_each_node=True,
fp16=False
)
# %%
trainer = Trainer(
model=model,
args=args,
train_dataset=tokenized_id,
data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, padding=True),
)
trainer.train(resume_from_checkpoint=True)
# %%
trainer.save_model("./qwen_sft_full")
# %%
llm_model_name="Qwen/Qwen1.5-0.5B-Chat"
#model = AutoModelForCausalLM.from_pretrained(llm_model_name)
# # Load PEFT model on CPU
model = AutoPeftModelForCausalLM.from_pretrained(
"Qwen1.5_0.5B_Chat_sft_full_ckpt_200_ok/checkpoint-210",
#low_cpu_mem_usage=True,
)
# # Merge LoRA and base model and save
#merged_model = model.merge_and_unload()
#merged_model.save_pretrained("./qwen_sft",safe_serialization=False, max_shard_size="2GB")
tokenizer = AutoTokenizer.from_pretrained(llm_model_name,trust_remote_code=True, pad_token='<|endoftext|>')
# %%
prompt = "谜面：一生受用（猜一字）\n谜底是什么？请解释。"
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
print(text)
model_inputs = tokenizer([text], return_tensors="pt").to("cpu")
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=128,
do_sample=False,
top_p=0.0
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
# %%
response
# %%
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