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from transformers import AutoModelForCausalLM,GenerationConfig,AutoTokenizer,GPTQConfig
from peft import AutoPeftModelForCausalLM
from peft import PeftModel, PeftConfig
import torch
def input_data_preprocessing_1(example):
processed_example = "<|system|>\n You are a support chatbot who helps with user queries chatbot who always responds in the style of a professional.\n<|user|>\n" + example["instruction"] + "\n<|assistant|>\n"
return processed_example
def customerConverstaion(prompt):
def input_data_preprocessing(example):
processed_example = "<|system|>\n You are a support chatbot who helps with user queries chatbot who always responds in the style of a professional.\n<|user|>\n" + example["instruction"] + "\n<|assistant|>\n"
return processed_example
input_string = input_data_preprocessing(
{
"instruction": "i have a question about cancelling order {{Order Number}}",
}
)
tokenizer = AutoTokenizer.from_pretrained("zephyrFT/checkpoint-100")
model = AutoPeftModelForCausalLM.from_pretrained("zephyrFT/checkpoint-100",
low_cpu_mem_usage=True,
return_dict=True,
torch_dtype=torch.float16,
device_map="cuda")
inputs = tokenizer(input_string, return_tensors="pt").to("cuda")
generation_config = GenerationConfig(
do_sample=True,
top_k=1,
temperature=0.1,
max_new_tokens=256,
pad_token_id=tokenizer.eos_token_id
)
outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
def customerConverstaion_1(prompt):
# Check GPU availability
print("Available GPU devices:", torch.cuda.device_count())
print("Name of the first available GPU:", torch.cuda.get_device_name(0))
config = PeftConfig.from_pretrained("DSU-FDP/customer-support")
base_model = AutoModelForCausalLM.from_pretrained("TheBloke/zephyr-7B-beta-GPTQ", device_map='cuda')
model = PeftModel.from_pretrained(base_model, "DSU-FDP/customer-support")
from transformers import AutoTokenizer,GPTQConfig
tokenizer=AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
tokenizer.padding_side = 'right'
tokenizer.pad_token = tokenizer.eos_token
tokenizer.add_eos_token = True
tokenizer.add_bos_token, tokenizer.add_eos_token
tokenizer = AutoTokenizer.from_pretrained("DSU-FDP/customer-support")
input_string = input_data_preprocessing(
{
"instruction": "i have a question about cancelling order {{Order Number}}",
}
)
inputs = tokenizer(input_string, return_tensors="pt").to("cuda")
generation_config = GenerationConfig(
do_sample=True,
top_k=1,
temperature=0.1,
max_new_tokens=256,
pad_token_id=tokenizer.eos_token_id
)
outputs = model.generate(**inputs, generation_config=generation_config)
return outputs
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