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Н Новости

Дообучаем Llama 3.1 у себя дома

Привет, чемпионы! С каждым днем появляется все больше и больше LLM, их показатели растут с таким же темпом. Но в определенных областях они до сих пор не слишком хороши, так как на этапе предобучения не выгодно охватывать узкие домены - легче модели скормить общие данные, а потом дообучить.

Секунду, слишком много терминов на один пиксель: что такое предобучение и "дообучение"? Практически все современные "чатовые" модели выпускаются не до конца обученные, чтобы в случае неудовлетворенности пользователя результатам генерации, он смог дообучить под свои данные.

Дообучение - процесс процесс дальнейшего обучения предварительно обученной языковой модели с использованием дополнительных данных.

Совсем недавно вышла Llama 3.1, вокруг которой очень много хайпа и показатели у нее совсем неплохие. Так давайте в качестве базовой модели возьмем именно ее. А данные возьмем медицинского характера и из LLM сделаем ассистента, которой сможет вас проконсультировать в любой момент.

Бенчмарки Llama 3.1 с конкурентами

Возьмем данные с Hugging face 🤗. У нас имеется вопрос пациента и дальнейший ответ квалифицированного работника. Хотим, чтобы наша модель смогла отвечать также, как и медик, так что не медлим и приступаем к дообучению!

Что сегодня будем использовать?

Сегодня закодим дообучение на python так, чтобы оно работало без бубнов и прочей атрибутики плохих программистов. Поэтому будем использовать только хорошие фреймворки: Transformers 🤗, PEFT, Trl.

Transformers 🤗 - поможет нам скачать веса модели без лишней мороки, PEFT - жизненно необходим, если хотим чтобы все влезло на GPU. Про Trl расскажу чуть позже. И остальное по мелочи

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%pip install -U transformers # для использования модели
%pip install -U datasets # для подгрузки тестового датасетов
%pip install -U accelerate # вспомогательная библиотека
%pip install -U peft # QLoRA
%pip install -U trl # super-vised тренер
%pip install -U bitsandbytes # оптимизация весов модели
%pip install -U wandb # храним логи правильно

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import os, torch, wandb

from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
    HfArgumentParser,
    TrainingArguments,
    pipeline,
    logging,
)
from peft import (
    LoraConfig,
    PeftModel,
    prepare_model_for_kbit_training,
    get_peft_model,
)

from datasets import load_dataset
from trl import SFTTrainer, setup_chat_format
from dataclasses import dataclass

Тот самый DS маминой подруги

Также сегодня будем использовать Weights & Bias- это очень мощных инструмент!Советую вам его освоить чтобы не залипать в экран, пока LLM дообучится (а процесс этот очень долгий). Также если вы будете запускать код на Kaggle, то рекомендую подгружать все ключи через плагин Secrets. После того как секреты затаили, делаем также:

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from huggingface_hub import login
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()

hf_token = user_secrets.get_secret("huggingface_token")

login(token = hf_token)

wb_token = user_secrets.get_secret("wandb_api_key")

wandb.login(key=wb_token)
run = wandb.init(
    project='Fine-tune Llama 3.1 8B on Medical Dataset', 
    job_type="training", 
    anonymous="allow"
)

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@dataclass
class Config:
    model_name = "meta-llama/Meta-Llama-3.1-8B-Instruct"
    dataset_name = "ruslanmv/ai-medical-chatbot"
    new_model = "llama-3.1-8b-chat-doctor"
    torch_dtype = torch.float16
    attn_implementation = "eager"


# напишем вспомогательный класс для хранений атрибутов
cfg = Config() 

# cfg можно будет потом использовать вот так удобно
tokenizer = AutoTokenizer.from_pretrained(cfg.model_name)

model, tokenizer = setup_chat_format(model, tokenizer)
tokenizer.padding_side = 'right'
tokenizer.padding_token = '<|pad|>'

Также создадим все классы для обучения модели. Так как у нас нет времени для полного обучения LLM, то мы воспользуемся эффективным по памяти адаптером Q-LoRA, которая позволяет поместить модель на видеокарту 16GB.

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# QLoRA config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch_dtype,
    bnb_4bit_use_double_quant=True,
)

# Load model
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=bnb_config,
    device_map="auto",
    attn_implementation=attn_implementation
)

# LoRA config / адаптер 
peft_config = LoraConfig(
    r=16,
    lora_alpha=32,
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
    target_modules=['up_proj', 'down_proj', 'gate_proj', 'k_proj', 'q_proj', 'v_proj', 'o_proj']
)
# Сам адаптер удобно использовать при создании тренера,
# так что читаем дальше

И финишная прямая в подготовке к обучению это подгрузка данных. Так как мы обучаем instruct- модель, то соберем данные сразу же в формат чата. Это позволит модели сконцетрироваться над самим ответом на вопрос - то что нужно в нашей задаче.

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def format_chat_template(row):
    row_json = [{"role": "user", "content": row["Patient"]},
               {"role": "assistant", "content": row["Doctor"]}]
    row["text"] = tokenizer.apply_chat_template(row_json, tokenize=False)
    return row


# Библиотека Datasets 🤗 очень приятна в использовании, 
# когда используете LLM. Так что берите на вооружение, чемпионы!
dataset = load_dataset(cfg.dataset_name, split="all")

dataset = dataset.map(
	format_chat_template,
	num_proc=4,
)


# Также для учебных целей урежем исходный датасет,
# чтобы сравнить результаты - читаем в самом конце
dataset_sh = dataset.shuffle(seed=2024).select(range(10_000))

Fine tune-им LLM модельку

Так как сегодня создаем универсальный ноутбук для обучения любой LLM, то однозначно будем использовать TrainingArguments - это класс, который сохраняет все ваши параметры для обучения: размер батча, оптимизатор, шедулер, логи - и все остальные необходимые вещи:

training_arguments = TrainingArguments(
    output_dir=cfg.new_model,
    per_device_train_batch_size=1,
    per_device_eval_batch_size=1,
    gradient_accumulation_steps=2,
    optim="paged_adamw_32bit",
    num_train_epochs=1,
    eval_strategy="steps",
    eval_steps=0.2,
    logging_steps=100,
    warmup_steps=10,
    logging_strategy="steps",
    learning_rate=2e-4,
    fp16=False,
    bf16=False,
    group_by_length=True,
    report_to="wandb"
)

И наконец-то создаем тренера, который будет учить нашу модельку взаимодействовать с датасетом. И так как у нас нет как таковых ответов, то мы будем использовать SFT - supervised fine-tuning. Обучение без учителя, как раз нам подойдет. Так как нам не придется писать свою reward модель, которой необходимо оценивать ответ Llama. Чтобы не изобретать велосипед SFTTrainer возьмем из trl:

trainer = SFTTrainer(
    model=model,
    train_dataset=dataset_sh["train"],
    eval_dataset=dataset_sh["test"],
    peft_config=peft_config, # сам адаптер, который создали ранее
    max_seq_length=512,
    dataset_text_field="text",
    tokenizer=tokenizer, # был импортирован 
    args=training_arguments,
    packing= False,
)

trainer.train() # Запускаем обучение

Фраза взята из записей 12 апреля 1961 года

Подождав примерно вечность (35ч) мы получим дообученную Llama 3.1-instruct 8b на медицинских данных. Давайте тестировать, действительно-ли качество улучшилось и есть вообще смысл в наших действиях

Тестим результат дообучения

Сперва давайте спросим что-то простое и жизненное, например: I have severe headaches help me please и сравним как ответы моделей.

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I'm sorry to hear that you're experiencing severe headaches. While I can provide some general advice, it's important to note that I’m not a substitute for medical advice from a healthcare professional. If your headache is sudden, severe, or accompanied by other symptoms like vision changes, confusion, difficulty speaking, or weakness, you should seek immediate medical attention, as these could be signs of a serious condition.

Недообученная Llama 3 сразу нам отвечает, что она не является медицинским сотрудником и дает нам расплывчатый ответ. Смотрим как отвечает наш "врач":

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As a medical professional, I would conduct a thorough evaluation to determine the cause of your headaches. This would include:
Medical history: A thorough review of your medical history, including any previous diagnoses, medications, and allergies.
Physical examination: A physical examination to assess your overall health and look for any signs of underlying conditions that may be contributing to your headaches.
Imaging tests: Imaging tests such as CT or MRI scans may be ordered to rule out any underlying conditions such as tumors, aneurysms, or other structural abnormalities.
Blood tests: Blood tests may be ordered to check for any underlying conditions such as infections, inflammation, or hormonal imbalances.
Treatment Options:
Based on the results of the evaluation, the following treatment options may be recommended:
Pain relief medications: Over-the-counter or prescription pain medications such as triptans, ergots, or corticosteroids may be recommended to help manage headache pain.
Migraine medications: If you are diagnosed with migraines, medications such as triptans or ergots may be prescribed to help manage symptoms.
Muscle relaxants: Muscle relaxants such as cyclobenzaprine or baclofen may be prescribed to help relax tense muscles and reduce headache pain.
Antidepressants: Antidepressants such as amitriptyline or nortriptyline may be prescribed to help manage stress and anxiety that may be contributing to headaches.
Lifestyle modifications: Lifestyle modifications such as maintaining a consistent sleep schedule, staying hydrated, and avoiding triggers may be recommended to help manage headaches.
Headache Prevention:
To prevent headaches, the following strategies may be recommended:
Maintain a headache diary: Keeping a headache diary can help identify triggers and patterns of headache occurrence.
Avoid triggers: Avoiding triggers such as certain foods, bright lights, or loud noises can help prevent headaches.
Stay hydrated: Drinking plenty of water throughout the day can help prevent dehydration headaches.
Exercise regularly: Regular exercise can help reduce stress and anxiety and improve overall health.
Manage stress: Stress management techniques such as meditation, deep breathing, or yoga can help reduce stress and anxiety that may contribute to headaches.

✅ О чудо! Дообученная модель выдала нам возможные причины, их последствия и как это можно лечить

Усложним вопрос: I have a suspiciously large mole. Could I have cancer? How can I determine this at home?

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I can't provide medical advice or diagnose skin conditions, including cancer. If you are concerned about a mole, I suggest you schedule an appointment with a dermatologist. They can examine the mole, take a medical history, and perform any necessary tests to determine if the mole is cancerous.

Сейчас вообще отказалась отвечать на наш вопрос 😠

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As a dermatologist, I must emphasize that while it's possible to evaluate your mole at home, a thorough examination by a dermatologist is essential for an accurate diagnosis. That being said, I'll provide you with a detailed guide on how to assess your mole at home, and what to look for.
I use the ABCDE rule to evaluate moles for potential cancer risk:
A - Asymmetry: If the mole is not symmetrical, it could be a sign of cancer. Imagine drawing a line down the middle of the mole. If the two sides are not mirror images of each other, it's asymmetrical.
B - Border: A mole with an irregular, notched, or scalloped border may be a sign of cancer. A healthy mole has a smooth, well-defined border.
C - Color: Moles with multiple colors or an uneven color distribution could be a sign of cancer. Healthy moles are usually one color, such as brown, tan, or black.
D - Diameter: Moles that are larger in diameter than a pencil eraser (about 6mm) could be a sign of cancer. However, size alone is not a reliable indicator of cancer.
E - Evolving: If the mole is changing in size, shape, or color, it could be a sign of cancer.

Здорово! И пусть дальше врачи разбираются, насколько это адекватный ответ. "Наши полномочия на этом - все".

Заключение и вывод

Дообучение LLM - можно превратить в приятный процесс. Выше приведенный универсальный код позволит вашим моделям лучше отвечать на определенную область знаний. Если вам понадобилось улучшать качество модели и RAG не помогает, то один из путей решения - Fine Tuning.

Больше про AI и LLM вы сможете найти в моем тг канале, где я кстати опубликовал ноутбук полным кодом из сегодняшней статьи 😉

Счастливого LLM-га!

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28 авг. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
08.10.23 01:30 davec8080

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13.07.24 21:13 michaelharrell825

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17.07.24 02:26 thompsonrickey

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27.08.24 12:50 James889900

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02.09.24 20:24 [email protected]

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06.09.24 01:35 Celinagarcia

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06.09.24 01:44 Celinagarcia

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16.09.24 00:10 marcusaustin

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16.09.24 00:11 marcusaustin

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23.09.24 18:56 matthewshimself

At first, I was admittedly skeptical about Worldcoin (ref: https://worldcoin.org/blog/worldcoin/this-is-worldcoin-video-explainer-series), particularly around the use of biometric data and the WLD token as a reward mechanism for it. However, after following the project closer, I’ve come to appreciate the broader vision and see the value in the underlying tech behind it. The concept of Proof of Personhood (ref: https://worldcoin.org/blog/worldcoin/proof-of-personhood-what-it-is-why-its-needed) has definitely caught my attention, and does seem like a crucial step towards tackling growing issues like bots, deepfakes, and identity fraud. Sam Altman’s vision is nothing short of ambitious, but I do think he & Alex Blania have the chops to realize it as mainstay in the global economy.
01.10.24 14:54 Sinewclaudia

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02.10.24 22:27 Emily Hunter

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
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31.10.24 00:13 ytre89

Can those who have fallen victim to fraud get their money back? Yes, you might be able to get back what was taken from you if you fell prey to a fraud from an unregulated investing platform or any other scam, but only if you report it to the relevant authorities. With the right plan and supporting documentation, you can get back what you've lost. Most likely, the individuals in control of these unregulated platforms would attempt to convince you that what happened to your money was a sad accident when, in fact, it was a highly skilled heist. You should be aware that there are resources out there to help you if you or someone you know has experienced one of these circumstances. Do a search using (deftrecoup (.) c o m). Do not let the perpetrators of this hoaxes get away with ruining you mentally and financially.
02.11.24 14:44 diannamendoza732

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