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import os
import torch
from datasets import load_dataset
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
TrainingArguments,
pipeline,
)
from peft import LoraConfig
from trl import SFTTrainer
from tqdm import tqdm
from Rebe_QA_data_scrape_english_site import append_dict_to_csv
compute_dtype = getattr(torch, "float16")
quant_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=False,
)
#######################
### Load Base Model ###
#######################
base_model_name = "tomer-shimshi/llama2-Rav" #"results_fine_tune_after_shulhan_aruch_no_heb_V4\llama-2" #"tomer-shimshi/llama2-Rav" #os.path.join(os.getcwd() ,"results_fine_tune_after_shulhan_aruch_no_heb_V3\llama-2")
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
quantization_config=quant_config,
device_map="auto"#{"": 0}
)
######################
### Load Tokenizer ###
######################
tokenizer = AutoTokenizer.from_pretrained(
base_model_name,
trust_remote_code=True
)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "right"
##############################
### Set Saving Arguments ###
##############################
save_path_csv_path = os.path.join(os.getcwd() , "dataset","usage_dataset.csv")
alpaca_prompt = """you are a jewish Rav, please answer the following question according to the Halakha (Jewish law) .
### Question:
{}
### Answer:
{}"""
def formatting_prompts_func(examples):
inputs = examples["question"]
outputs = examples["answer"]
texts = []
#global EOS_TOKEN
for input, output in zip( inputs, outputs):
# Must add EOS_TOKEN, otherwise your generation will go on forever!
#text = alpaca_prompt.format(instruction, input, output) #+ EOS_TOKEN
text = alpaca_prompt.format( input, output) #+ EOS_TOKEN
texts.append(text)
return { "text" : texts, }
question = input('Please enter a question for the Rav \n Enter empty string to quit \n')
while len(question)>1:
pipe = pipeline(
task="text-generation",
model=model,
tokenizer=tokenizer,
#eos_token_id=EOS_TOKEN,
repetition_penalty = 2.0,
do_sample = True,
max_new_tokens = 600,
top_k=10,
#num_return_sequences=1,
)
model_prompt = alpaca_prompt.format( question, "")
result = pipe(model_prompt)
actual_output = result[0]['generated_text'].split("### Answer:")[1].replace('/r','').replace('\n','')
#append_dict_to_csv(save_dict, save_path_csv_path)
print(f"The Rav answer is {actual_output} \n \n")
question = input('Please enter a question for the Rav \n Enter empty string to quit \n')#item['question']#test_dataset['quastion'][i]
#"We offer a 30-day full refund at no extra cost."
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