library_name: peft
base_model: NousResearch/Llama-2-7b-chat-hf
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#code
'''python
#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