Llama-2-7b-chat-finetune-for-textGeneration

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#code

#testing and loading model

import torch, gc
gc.collect()
torch.cuda.empty_cache()

import numpy as np
import pandas as pd
import os
from tqdm import tqdm
import bitsandbytes as bnb
import torch
import torch.nn as nn
import transformers
from datasets import Dataset
from peft import LoraConfig, PeftConfig
from trl import SFTTrainer
from transformers import (AutoModelForCausalLM,
                          AutoTokenizer,
                          BitsAndBytesConfig,
                          TrainingArguments,
                          pipeline,
                          logging)
from sklearn.metrics import (accuracy_score,
                             classification_report,
                             confusion_matrix)
from sklearn.model_selection import train_test_split

from datasets import load_dataset
from peft import LoraConfig, PeftModel

device_map = {"": 0}
PEFT_MODEL = "kr-manish/Llama-2-7b-chat-finetune-for-textGeneration"
#model_name = "NousResearch/Llama-2-7b-hf"

config = PeftConfig.from_pretrained(PEFT_MODEL)

model = AutoModelForCausalLM.from_pretrained(
    config.base_model_name_or_path,
    low_cpu_mem_usage=True,
    return_dict=True,
    #quantization_config=bnb_config,
    device_map="auto",
    #trust_remote_code=True,
    torch_dtype=torch.float16,
)

tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
tokenizer.pad_token = tokenizer.eos_token

load_model = PeftModel.from_pretrained(model, PEFT_MODEL)

test1 ="How to own a plane in the United States?"
prompt_test = test1
pipe_test = pipeline(task="text-generation",
                model=load_model,
                tokenizer=tokenizer,
                #max_length =20,
                max_new_tokens =25,
                temperature = 0.0,
                
                )
result_test = pipe_test(prompt_test)
#answer = result[0]['generated_text'].split("=")[-1]
answer_test = result_test[0]['generated_text']
answer_test

#How to own a plane in the United States?\n\nIn the United States, owning a plane is a significant investment and requires careful planning and research. Here are

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.10.0