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# -*- coding: utf-8 -*-
"""Mistral_fine-tuning.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/12WLPo_Aicjhp7sy8zbZmfg5behAZJVYN
"""
!pip install -q -U bitsandbytes
!pip install -q -U git+https://github.com/huggingface/transformers.git
!pip install -q -U git+https://github.com/huggingface/peft.git
!pip install -q -U git+https://github.com/huggingface/accelerate.git
!pip install -q -U datasets scipy ipywidgets matplotlib
from datasets import load_dataset
dataset = load_dataset("Devdeshitha/testdataset1")
from datasets import load_dataset
train_dataset = load_dataset('json', data_files='notes.jsonl', split='train')
eval_dataset = load_dataset('json', data_files='notes_validation.jsonl', split='train')
def formatting_func(example):
text = f"### Question: {example['input']}\n ### Answer: {example['output']}"
return text
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
base_model_id = "mistralai/Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config)
tokenizer = AutoTokenizer.from_pretrained(
base_model_id,
padding_side="left",
add_eos_token=True,
add_bos_token=True,
)
tokenizer.pad_token = tokenizer.eos_token
def generate_and_tokenize_prompt(prompt):
return tokenizer(formatting_func(prompt))
tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt)
tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt)
max_length = 1024 #This was an appropriate max length for my dataset
def generate_and_tokenize_prompt2(prompt):
result = tokenizer(
formatting_func(prompt),
truncation=True,
max_length=max_length,
padding="max_length",
)
result["labels"] = result["input_ids"].copy()
return result
tokenized_train_dataset = train_dataset.map(generate_and_tokenize_prompt2)
tokenized_val_dataset = eval_dataset.map(generate_and_tokenize_prompt2)
print(tokenized_train_dataset[1]['input_ids'])
eval_prompt = "How does consolidating credit cards into a loan work?: "
# Re-init the tokenizer so it doesn't add padding or eos token
tokenizer = AutoTokenizer.from_pretrained(
base_model_id,
add_bos_token=True,
)
model_input = tokenizer(eval_prompt, return_tensors="pt").to("cuda")
model.eval()
with torch.no_grad():
print(tokenizer.decode(model.generate(**model_input, max_new_tokens=512, repetition_penalty=1.15)[0], skip_special_tokens=True))
"""Set Up LoRA
Now, to start our fine-tuning, we have to apply some preprocessing to the model to prepare it for training. For that use the prepare_model_for_kbit_training method from PEFT.
""" |