Hagaha Bilowga ah ee Hagaajinta Moodellada Luqadda Weyn (Fine-tuning): Fikradaha, Hababka iyo Ku-dhaqanka

Moodellada luqadda ee waaweyn (LLMs) waxay sameeyeen horumar la taaban karo oo ku saabsan goobta farsamaynta luqadda dabiiciga ah, waxayna si fiican u qabteen jiilka qoraalka, tarjumaadda, su'aalaha iyo jawaabaha, iwm. Si kastaba ha noqotee, si moodelladani ay si fiican ugu shaqeeyaan hawlo ama goobo gaar ah, hagaajinta (Fine-tuning) waxay noqotay farsamo muhiim ah. Maqaalkani wuxuu si qoto dheer u sahamin doonaa fikradaha, hababka, iyo codsiyada dhabta ah ee hagaajinta LLM, si looga caawiyo bilowgayaasha inay si dhakhso ah u bilaabaan.

Waa maxay Hagaajinta?

Hagaajintu waxay tixraacaysaa tababar dheeraad ah oo lagu sameeyo moodellada luqadda ee waaweyn ee hore loo tababaray iyadoo la isticmaalayo xog-ururin hawleed gaar ah. Moodellada hore loo tababaray waxay horey u barteen aqoon luqadeed oo guud, halka hagaajintu ay ka dhigayso inay la qabsadaan faahfaahinta iyo qaababka hawl gaar ah. Bal qiyaas in moodalka hore loo tababaray uu yahay encyclopedia, oo ay ku jiraan aqoon ballaaran. Hagaajintu waxay la mid tahay in moodalka la siiyo buug si gaar ah uga hadlaya "daawada", taas oo ka dhigaysa mid xirfad badan goobta caafimaadka.

Isbarbardhigga Hagaajinta iyo Tababarka Laga Bilaabo:

• Tababarka Laga Bilaabo: Wuxuu u baahan yahay ilo xisaabeed iyo xog badan, iyo waqti tababar oo dheer.
• Hagaajinta: Waxay u baahan tahay xog yar iyo ilo xisaabeed, waqti tababar oo gaaban, waxayna badanaa gaartaa natiijooyin ka wanaagsan.

Maxaa loo Sameeyaa Hagaajinta?

• Kor u qaadida Waxqabadka: Ka dhigida moodalka inuu si fiican u shaqeeyo hawlo gaar ah, sida falanqaynta dareenka, kala soocidda qoraalka, tarjumaadda mishiinka, iwm.
• La qabsashada Goobta: Ka dhigida moodalka inuu la qabsado aqoonta iyo qaabka goob gaar ah, sida maaliyadda, sharciga, caafimaadka, iwm.
• Kaydinta Kheyraadka: Marka la barbar dhigo tababarka laga bilaabo, hagaajintu waxay si weyn u yareyn kartaa ilaha xisaabeed iyo kharashyada waqtiga.
• Xakamaynta: U oggolaanshaha horumariyeyaasha inay si fiican u xakameeyaan qaabka wax soo saarka iyo dabeecadda moodalka.

Tallaabooyinka Muhiimka ah ee Hagaajinta

1. Xulashada Moodalka Hore loo Tababaray: Xulo moodal hore loo tababaray oo ku habboon hawsha. Tusaale ahaan, hawlaha abuurista qoraalka, waxaad dooran kartaa moodellada taxanaha GPT; hawlaha su'aalaha iyo jawaabaha, waxaad dooran kartaa moodellada taxanaha BERT. Hugging Face Model Hub (https://huggingface.co/models) waa il wanaagsan oo laga heli karo moodello kala duwan oo hore loo tababaray.

2. Diyaarinta Xog-ururinta: Diyaari xog-ururin hawleed gaar ah oo tayo sare leh. Cabbirka iyo tayada xog-ururinta ayaa saameyn weyn ku leh saameynta hagaajinta.

   • Nadiifinta Xogta: Nadiifi khaladaadka, buuqa, iyo iswaafaq la'aanta xogta.
   • Calaamadaynta Xogta: Calaamadee xogta, tusaale ahaan, kala soocidda qoraalka waxay u baahan tahay calaamadaynta qaybaha, hawlaha su'aalaha iyo jawaabaha waxay u baahan yihiin calaamadaynta jawaabaha.
   • Qaybinta Xogta: U qaybi xog-ururinta tababar, ansaxin, iyo xog-ururin tijaabo ah.

3. Habaynta Halbeegyada Hagaajinta: Xulo hagaajiyaha ku habboon, heerka waxbarashada, cabbirka dufcadda, epochs tababarka, iyo halbeegyo kale.

   • Heerka Waxbarashada: Heerka waxbarashada wuxuu xakameynayaa xawaaraha moodalka uu ku cusbooneysiinayo halbeegyada. Heerka waxbarashada oo aad u sarreeya wuxuu sababi karaa in moodalka uu noqdo mid aan degganeyn, heerka waxbarashada oo aad u hooseeyana wuxuu sababi karaa tababar gaabis ah. Qiimaha heerka waxbarashada ee caadiga ah waxaa ka mid ah: 1e-3, 1e-4, 1e-5.
   • Cabbirka Dufcadda: Cabbirka dufcadda wuxuu go'aamiyaa tirada muunadaha loo isticmaalo tababar kasta oo isdaba joog ah. Cabbirka dufcadda oo weyn wuxuu kordhin karaa xawaaraha tababarka, laakiin wuxuu qaadan karaa xusuus badan.
   • Epochs: Epochs waxay tixraacdaa tirada jeer ee xog-ururinta tababarka oo dhan ay dhex marto moodalka. Epochs badan waxay sababi karaan ku-habboonaan xad dhaaf ah, epochs yarina waxay sababi karaan tababar aan ku filneyn.

4. Samee Hagaajinta: Isticmaal xog-ururinta la diyaariyay iyo halbeegyada qaabeynta si aad u hagaajiso moodalka hore loo tababaray. Qaababka hagaajinta ee caadiga ah waxaa ka mid ah TensorFlow, PyTorch, iyo Hugging Face Transformers.

5. Qiimee Moodalka: Isticmaal xog-ururinta tijaabada si aad u qiimeyso waxqabadka moodalka la hagaajiyay, oo samee hagaajin kasta oo lagama maarmaan ah. Halbeegyada qiimeynta ee caadiga ah waxaa ka mid ah saxsanaanta, saxnaanta, soo celinta, qiimaha F1, iwm.

Hababka Hagaajinta

1. Hagaajin Buuxda (Full Fine-tuning)

Kani waa habka hagaajinta ee ugu tooska badan, wuxuuna cusbooneysiiyaa dhammaan halbeegyada moodalka hore loo tababaray.

• Faa'iidooyinka: Waxay si buuxda uga faa'iideysan kartaa aqoonta moodalka hore loo tababaray, waxayna gaari kartaa waxqabadka ugu fiican hawl gaar ah.
• Qasaarooyinka: Waxay u baahan tahay ilo xisaabeed iyo xusuus badan, wayna fududahay in la ku-habboonaado xad dhaaf ah.

2. Hagaajinta Wanaagsan ee Hufan ee Halbeegyada (Parameter-Efficient Fine-tuning, PEFT)

Maadaama moodellada waaweyn ay leeyihiin halbeegyo badan, hagaajinta wanaagsan ee buuxda waxay leedahay kharash badan. Hababka hagaajinta wanaagsan ee hufan ee halbeegyada waxay cusbooneysiiyaan oo keliya qayb yar oo ka mid ah halbeegyada moodelka, taasoo yareynaysa kharashka xisaabinta iyo baahida xusuusta.

• LoRA (Low-Rank Adaptation)

  LoRA waxay soo bandhigtaa matrix hoose si ay ugu dhowaato cusbooneysiinta halbeegyada moodelka asalka ah. Fikradda ugu weyn waa in lagu daro matrix hoose oo ku xiga matrix miisaanka jira ee moodelka hore loo tababaray, oo lagu hagaajiyo hawlaha hoose iyadoo la tababarayo matrix-yada hoose. Sidan, halbeegyo yar oo keliya ayaa u baahan in la tababaro, taasoo si weyn u yareynaysa kharashka xisaabinta.

  # Isticmaal maktabadda Hugging Face PEFT si aad u sameyso hagaajin wanaagsan oo LoRA ah
  from peft import LoraConfig, get_peft_model
  
  # Qeex qaabeynta LoRA
  lora_config = LoraConfig(
      r=8, # Darajada matrix-ka hoose
      lora_alpha=32, # Qodobka miisaanka LoRA
      lora_dropout=0.05, # Suurtagalnimada LoRA ee ka bixitaanka
      bias="none",
      task_type="CAUSAL_LM" # Nooca hawsha
  )
  
  # Soo rar moodelka hore loo tababaray
  model = AutoModelForCausalLM.from_pretrained(model_name_or_path)
  
  # Ku dabaq LoRA moodelka
  model = get_peft_model(model, lora_config)
  model.print_trainable_parameters()
  

• Hagaajinta Horgale (Prefix Tuning)

  Hagaajinta Horgale waxay ku dartaa dhowr vector "horgale" oo la tababari karo isku xigxiga gelinta, waxayna hagaajisaa habdhaqanka moodelka iyadoo la tababarayo vector-yada horgale. Habkani uma baahna in la beddelo halbeegyada moodelka asalka ah, sidaas darteed aad ayuu u hufan yahay.

• Hagaajinta Adapter (Adapter Tuning)

  Hagaajinta Adapter waxay gelisaa dhowr module oo shabakad neerfeed oo yaryar (adapters) lakab kasta oo ka mid ah moodelka hore loo tababaray, waxayna hagaajisaa hawlaha hoose iyadoo la tababarayo adapters-ka. Marka la barbar dhigo hagaajinta wanaagsan ee buuxda, Hagaajinta Adapter waxay u baahan tahay oo keliya in la tababaro halbeegyo yar, iyadoo la ilaalinayo waxqabad wanaagsan.

3. Hagaajinta Degdegga ah (Prompt Tuning)

Hagaajinta Degdegga ah waa hab hagaajin wanaagsan oo fudud, kaasoo hagaya moodelka hore loo tababaray si uu u soo saaro wax soo saarka la filayo iyadoo la hagaajinayo tilmaamaha gelinta (prompt). Habkani uma baahna in la beddelo halbeeg kasta oo ka mid ah moodelka, sidaas darteed aad ayuu u hufan yahay.

• Hagaajinta Degdegga ah ee Adag (Hard Prompt Tuning): Naqshadeynta degdegga ah gacanta.
• Hagaajinta Degdegga ah ee Jilicsan (Soft Prompt Tuning): Isticmaal vector-yo la tababari karo sida degdeg ah, oo hagaaji degdegga adigoo tababaraya vector-yadan.

# Isticmaal degdeg la tababari karo (Soft Prompt)
from peft import PromptTuningConfig, get_peft_model, PromptTuningInit, TaskType

# Qeex qaabeynta Hagaajinta Degdegga ah
prompt_tuning_config = PromptTuningConfig(
    task_type=TaskType.CAUSAL_LM,
    prompt_tuning_init=PromptTuningInit.TEXT,
    num_virtual_tokens=20, # Dhererka degdegga ah
    prompt_tuning_init_text="Ka jawaab su'aalaha soo socda:", # Degdegga bilowga ah
    tokenizer_name_or_path=model_name_or_path,
)
```# 加载预训练模型
model = AutoModelForCausalLM.from_pretrained(model_name_or_path)

# 将 Prompt Tuning 应用于模型
model = get_peft_model(model, prompt_tuning_config)
model.print_trainable_parameters()

Talada Faa'iido Leh

• Kobcinta Xogta: Kordhi kala duwanaanshaha xogta tababarka adiga oo sameeya isbeddelo aan kala sooc lahayn, sida beddelka ereyada isku macnaha ah, dib u habeynta jumlada, iwm., si looga hortago ku-habboonaanta xad dhaafka ah.
• Joojinta Hore (Early Stopping): Inta lagu jiro tababarka, la soco waxqabadka xogta ansixinta, oo jooji tababarka goor hore marka waxqabadku uusan sii fiicnayn, si looga hortago ku-habboonaanta xad dhaafka ah.
• Hoos u dhigista Heerka Barashada (Learning Rate Decay): Inta lagu jiro tababarka, si tartiib tartiib ah u yaree heerka barashada, taas oo ka dhigi karta moodeelka mid si deggan ugu soo urura xalka ugu fiican.
• Nidaaminta (Regularization): Isticmaal L1 ama L2 nidaaminta si loo xakameeyo cabbirrada moodeelka, si looga hortago ku-habboonaanta xad dhaafka ah.
• Isticmaal Gelinta Horay Loo Tababaray (Pre-trained Embedding): Tusaale ahaan, GloVe ama Word2Vec, waxay kordhin karaan awoodda guud ee moodeelka.

Qalabka Lagu Taliyay

• Hugging Face Transformers: Waxay bixisaa moodeello horay loo tababaray oo badan iyo qalab hagaajin, waana qaabka ugu horreeya ee ay doorbidaan horumariyeyaasha LLM.
• PEFT (Parameter-Efficient Fine-Tuning): Maktabad Hugging Face ah, oo si gaar ah loogu talagalay hababka hagaajinta cabbirrada hufan.
• TensorBoard: Qalab loo isticmaalo in lagu sawiro habka tababarka, kaas oo kaa caawin kara inaad la socoto waxqabadka moodeelka oo aad hagaajiso cabbirrada.
• Weights & Biases: Madal loo isticmaalo in lagu raad raaco oo lagu sawiro tijaabooyinka barashada mashiinka.

Codsiyada Dhabta Ah

• Falanqaynta Dareenka: Hagaajinta LLM waxay hagaajin kartaa saxnaanta falanqaynta dareenka, sida aqoonsiga in dareenka ku jira faallooyinka filimku uu yahay mid togan ama mid taban.
• Qaybinta Qoraalka: Hagaajinta LLM waxaa loo isticmaali karaa hawlaha qaybinta qoraalka, sida u kala soocida maqaallada wararka qaybaha mawduucyo kala duwan.
• Tarjumaadda Mashiinka: Hagaajinta LLM waxay hagaajin kartaa tayada tarjumaadda mashiinka, sida tarjumaadda Ingiriisiga una tarjumaadda Shiinaha.
• Nidaamyada Su'aalaha iyo Jawaabaha: Hagaajinta LLM waxaa loo isticmaali karaa in lagu dhiso nidaamyada su'aalaha iyo jawaabaha, sida ka jawaabista su'aalaha ay soo jeediyaan isticmaalayaashu.
• Soosaarida Koodhka: Waxaad isticmaali kartaa LLM la hagaajiyay si aad u soo saarto qaybo koodh ah ama aad u dhammaystirto koodhka. Tusaale ahaan, GitHub Copilot waa tusaale codsi oo guuleystay.

Taxaddar

• Ku-Habboonaanta Xad Dhaafka Ah: Ku-habboonaanta xad dhaafka ah waxay u badan tahay inay dhacdo inta lagu jiro habka hagaajinta, waxaana loo baahan yahay in la qaado tallaabooyin u dhigma, sida kobcinta xogta, joojinta hore, nidaaminta, iwm.
• Hilmaanka Musiibada Leh (Catastrophic Forgetting): Hagaajinta waxay sababi kartaa in moodeelku uu iloobo aqoonta laga bartay marxaladda tababarka hore, waxaana loo baahan yahay in si taxaddar leh loo doorto xeeladaha hagaajinta.
• Eexda Xogta (Data Bias): Haddii xogta hagaajinta ay leedahay eex, waxay sababi kartaa in moodeelku uu si liidata u shaqeeyo kooxaha gaarka ah.
• Arrimaha Amniga: Moodeelka la hagaajiyay wuxuu soo saari karaa waxyaabo waxyeello leh ama aan habboonayn, waxaana loo baahan yahay in la sameeyo qiimeyn amni iyo shaandhayn.

Soo Koobid

LLM fine-tuning waa farsamo muhiim ah oo lagu wanaajiyo waxqabadka moodelka, lagana dhigo mid ku habboon hawlo iyo dhul gaar ah. Adigoo dooranaya moodel horay loo tababaray oo ku habboon, diyaarinaya xog-ururin tayo sare leh, habaynaya cabbirrada fine-tuning ee ku habboon, oo isku daraya farsamooyin wax ku ool ah oo kala duwan, waxaad si guul leh u samayn kartaa fine-tuning LLM, waxaadna ka gaari kartaa natiijooyin aad u wanaagsan xaalado codsi oo kala duwan. Maqaalkani wuxuu bixiyaa hage bilow ah, waxaana rajaynayaa inuu kaa caawin doono inaad si degdeg ah u bilowdo fine-tuning LLM. Iyadoo tignoolajiyadu ay sii socoto horumarka, waxaa jiri doona habab fine-tuning oo hufan oo ku habboon mustaqbalka.