SK Telecom (SKT) ne khamoshi se apni barri zabaan ke model (LLM) ko muta’arif karaya hai, jisay ‘A.X 4.0’ ke naam se jana jata hai. Is model ko ba-dast-e-khud Korean zabaan ki learning ko open-source framework mein shaamil kar ke banaya gaya tha. SKT ne jald hi inference-type model jari karne ka irada zahir kiya hai, jis ka preview version AOTX 4.1 May ke aakhir tak jari karne ka mansooba hai.
Telecommunications sector se April 23 ko yeh khabar aayi thi ke SKT ne AOTX 4.0 ko April 30 ko launch kiya hai, jisay GitHub par dastiyab kiya gaya hai, jo software development ke liye aik aam istemal hone wala platform hai. Aane wale inference model, AOTX 4.1 preview ki performance ke bare mein mazeed tafseelat bhi pehlay se hi share ki gayi thi.
AOTX 4.0 un koshisho ka nateeja hai jis ka zikar SKT ke CEO Yoo Young-sang ne pichlay mahinay kiya tha, unhoun ne kaha tha ke development mukammal hone ke qareeb hai. Is ke baad, yeh model aik mahinay ke andar finalise kar diya gaya aur filhal ise corporate services mein shaamil karne ka amal jari hai.
Is model ki bunyaad Alibaba ke Qwen 2.5 par mabni hai, jo China ki aik leading open-source LLM hai. AOTX 4.0 do version mein aata hai: aik standard model jis mein 72 billion parameters hain aur aik halka variant jis mein 7 billion parameters hain.
Korean Zabaan ke Liye Development aur Optimization
SKT ne is baat par zor diya ke unhoun ne aik aisa model banaya hai jo Korean context mein optimized performance faraham karta hai. Yeh Qwen 2.5 mein pehli shimahahi (quarter) mein wasee Korean data shaamil kar ke haasil kiya gaya. Korean information ko asani se process karne ke liye model ki salahiyat ko badhane ke liye, aik mukhtas Korean tokenizer implement kiya gaya tha.
SKT ki janib se jari kardah performance benchmarks se zahir hota hai ke AOTX 4.0 ne KMMLU benchmark mein 78.3 points score kiye. Yeh benchmark Korean zabaan ki maharat ki model ki samajh ko naapne ka kaam karta hai. Khaas tor par, AOTX 4.0 ne OpenAI ke GPT-4o ko outperforme kiya, jis ne 72.5 points, aur Alibaba ke Qwen 1.3 ne 70.6 points score kiye.
AOTX 4.1 Preview: Aik Inference-Type Model
AOTX 4.1 preview model, jisay May ke aakhir mein jari karne ka mansooba hai, aik inferential model ki numaindagi karta hai jis par SKT sargrami se kaam kar raha hai. Aik preview version jari kar ke, SKT official launch se pehlay model ki performance mein dilchaspi paida karne aur is ka jaiza lainay ka maqsad rakhta hai.
SKT ne is baat par roshni dali ke AOTX 4.1 preview model DeepSeek ke inference model, jisay ‘DeepSeek R1’ ke naam se jana jata hai, ki performance levels ke barabar performance dekhta hai. Is model ne saal ke shuru mein bohat tawajjah haasil ki thi.
Benchmark nataij AOTX 4.1 preview ka taqabul DeepSeek R1 se karte hue zahir karte hain ke AOTX 4.1 ne DeepSeek R1 ke size ke taqreeban nauween hissay hone ke bawajood aik jaisa score haasil kiya.
Mustaqbil ki Behtari aur Salahiyatein
Aagay dekhte hue, SKT ne AOTX 4.1 ke liye apne mansubay ka khulasa kiya, us ne kaha ke woh math problem-solving aur code development mein salahiyato ko badhaaay ga. Mazeed behtari coding abilities aur makhsoos sanati maharat par tawajjah markooz karegi. SKT aik agent-type model develop karne ka irada rakhta hai jo azadana tor par tasks ejaad kar sakta hai aur soch samjh kar faislay kar sakta hai.
Technical Specifications aur Architecture Mein Gehrai
A.X 4.0 sirf aik aur language model nahi hai; yeh aik ba-dast-e-khud tayyar kardah system hai jo Korean zabaan ke mahool mein behtareen performance ke liye tayyar kiya gaya hai. Is ki salahiyato ki puri tarah se qadar karne ke liye, hamein is ki technical specifications aur architectural choices ka jaiza lene ki zaroorat hai. Alibaba ke Qwen 2.5 par model ki bunyaad aik hikmat-e-amli ka faisla hai, jo aik mazboot, aalmi tor par manzoor shudah LLM ko starting point ke tor par istemal karta hai. Is bunyaad ko phir wasee Korean data se badhaya jata hai, jo Korean zabaan ki barikyo aur paichidgioun ke liye model ko fine-tune karta hai.
Dual-variant approach – aik standard model jis mein 72 billion parameters hain aur aik halka model jis mein 7 billion parameters hain – SKT ko applications ki wasee range ko pura karne ki ijazat deta hai. 72-billion-parameter model un tasks ke liye tayyar kiya gaya hai jin mein high precision aur gehri samajh ki zaroorat hoti hai, jab ke 7-billion-parameter model efficiency aur resource-constrained mahool mein deployment ke liye optimize kiya gaya hai. Yeh adaptability real-world applications ke liye ahem hai, jahan computational resources bohat mukhtalif ho sakte hain.
Korean Tokenizer: Aik Ahem Farq
A.X 4.0 ke ahem muqaabile karne walon mein se aik is ka makhsoos Korean tokenizer hai. Tokenization text ko chotay unit’on (tokens) mein tornay ka amal hai jisay model samajh aur process kar sakta hai. Riwayati tokenizers, aksar English ya deegar Latin-based zabaano mein train karte hain, Korean ke liye achi tarah se munasib nahi ho sakte kyunkay is ki apni zabaani khususiyaat hain, jaisay ke is ki agglutinative fitrat aur murakkab character structure (Hangul).
Korean-specific tokenizer implement kar ke, SKT yaqeen dahani karata hai ke A.X 4.0 Korean text ko asani se sambhal sakta hai. Yeh makhsoos tokenizer is liye tayyar kiya gaya hai:
- Hangul ko asani se sambhalein: Korean characters ko durusti se process aur numaindagi karein.
- Agglutination ko address karein: Murakkab alfaz ko un ke constituent morphemes (mafhomi unit’on) mein decompose karein.
- Contextual understanding ko behtar banayein: Korean jumlon mein alfaz ke darmiyan talluqat ko behtar tareeqay se pakrein.
Yeh optimized tokenization process machine translation, text summarization, aur sawal jawab daine jaisay tasks mein behtar performance mein barah-e-raast tabdeel ho jata hai.
Benchmarking A.X 4.0: Tawaquqaat Se Aagay
SKT ki janib se jari kardah performance benchmarks A.X 4.0 ki salahiyato ka mutharrik saboot faraham karte hain. KMMLU (Korean Massive Multitask Language Understanding) benchmark Korean zabaan ke wasee range ke tasks ke bare mein model ki samajh aur tadabbur karne ki salahiyat ka aik comprehensive evaluation hai. KMMLU benchmark par 78.3 ka score A.X 4.0 ko OpenAI ke GPT-4o (72.5) aur Alibaba ke Qwen 1.3 (70.6) se aagay rakhta hai, jo Korean zabaan ki maharat ki is ki fauqiat aur samajh ko zahir karta hai.
Yeh nataij khaas tor par qabil-e-zikr hain kyunkay woh A.X 4.0 ki sirf Korean text ko process karne ki salahiyat ko hi highlight nahi karte balkay bunyadi context aur maani ko bhi samajhtay hain. Yeh un tasks ke liye zaroori hai jin mein Korean culture aur society ke bare mein gehri tadabbur aur ilm ki zaroorat hoti hai.
AOTX 4.1 Preview: Inference Ka Wa’dah
AOTX 4.1 preview model ki aane wali release san’at mein khaasi utsahat paida kar rahi hai. Aik inference-type model ke tor par, AOTX 4.1 un tasks mein behtareen karkardagi dikhanay ke liye tayyar kiya gaya hai jin mein tadabbur, conclusion nikalna aur na-mukammal ya ambigious information se nataij haasil karne ki salahiyat ki zaroorat hoti hai. Yeh un applications ke liye ahem hai jaisay:
- Faislay karna: Data ka tajzia karna aur maloomat par mabni faislon ki himayat karne ke liye basirat faraham karna.
- Masail hal karna: Murakkab masail ki shanakht karna aur inhen hal karna.
- Predictive modeling: Tareekhi data aur rujhanaat ki bunyaad par mustaqbil ke nataij ka pishin goi karna.
SKT ka daawa hai ke AOTX 4.1 DeepSeek ke R1 model ke muqablay mein performance dekhta hai, halaankay is ka size bohat kam hai, is ki efficient architecture aur optimized training process ka saboot hai. Is se pata chalta hai ke AOTX 4.1 kam computational costs ke sath high performance day sakta hai, jis se yeh bohat se real-world applications ke liye ziyada amal daraamad ka hal ban jata hai.
Mustaqbil ke Liye SKT Ka Nuqta-e-Nazar: Agent-Type Models
AOTX 4.1 se aagay dekhte hue, SKT ke paas apni language models ki mustaqbil ki development ke liye ambitious mansubay hain. Company ke nuqta-e-nazar mein agent-type models tayyar karna shaamil hai jo azadana tasks ejaad kar saktay hain aur rational faislay kar saktay hain. Yeh artificial general intelligence (AGI) ki taraf aik ahem qadam ki numaindagi karta hai, jahan mashiinain koi bhi intellectual task kar sakti hain jo aik insan kar sakta hai.
Is hadaf ko haasil karne ke liye, SKT is baat par tawajjah markooz karne ka irada rakhta hai:
- Coding salahiyato ko mazboot karna: Model ko computer code tayyar karne aur samjhne ke qabil banana.
- Makhsoos sanati maharat ko badhana: Khas sectors se mutaliq specialized ilm par model ko train karna, jaisay finance, healthcare aur manufacturing.
- Tadabbur aur faislay karne ki salahiyato ko develop karna: Maloomat ka tajzia karne, options ka evaluation karne aur samajh boojh kar faislay karne ki salahiyat ke sath model ko mulsalah karna.
Agent-type models ki development mein bohat si sanato mein inqilab barpa karne, murakkab tasks ko automate karne, efficiency ko behtar bananay aur innovation ke liye naye imkanaat tayyar karne ki salahiyat hai.
Muqabilati Manzar Naama: SKT Ka Maqam
A.X 4.0 ke sath LLM space mein SK Telecom ka dakhla isay tezi se taqeeqat karne wali market mein aik ahem khiladi ki hesiyat se qaim karta hai. Aalmi tor par, OpenAI, Google aur Meta jaisi companies barri language models ko develop aur deploy karne mein bhari sarmaya kari kar rahi hain. Korea mein, Naver aur Kakao bhi ahem muqaabile hain.
Korean zabaan ki optimization par tawajjah markooz karne aur specialized models develop karne ki SKT ki hikmat-e-amli aik muqabilati faida faraham kar sakti hai. Apne models ko Korean market ki makhsoos zaruriyat ke mutabiq tayyar kar ke, SKT generic LLMs ko un tasks mein shikasat day sakta hai jin mein Korean zabaan, culture aur society ki gehri samajh ki zaroorat hoti hai.
Korean Maeeshat Ke Liye Asraat
A.X 4.0 aur deegar advanced language models ki development aur deployment ke Korean maeeshat ke liye ahem asraat ho sakte hain. In technologies mein yeh salahiyat hai:
- Paidawari ko badhana: Tasks ko automate karna, efficiency ko behtar banana aur insani workers ko ziyada creative aur hikmat-e-amli ki sargarmion par tawajjah markooz karne ke liye azad karna.
- Innovation ko chalana: Nai products, services aur business models ko qabil banana.
- Muqablayat ko badhana: Korean companies ko aalmi market mein ziyada asani se muqabla karne mein madad karna.
Korean hukoomat AI technologies ki development aur apnaane ko sargami se farogh day rahi hai, is baat ko tasleem karte hue ke un mein maeeshat ki taraqqi ko chalane aur zindagi ki quality ko behtar bananay ki salahiyat hai. LLMs mein SK Telecom ki sarmaya kari is qaumi hikmat-e-amli ke sath qadam milati hai aur Korea ki artificial intelligence ke maidan mein aik leader ke taur par ubharne mein madad kar sakti hai.
Akhlaqi Tarjeehat
Jaisay ke har powerful technology ke sath, barri language models ki development aur deployment ahem akhlaqi tarjeehatein uthati hain. In mein shaamil hain:
- Bias aur insaaf: Is baat ko yaqeen banana ke models ko mukhtalif aur numainda datasets par train kiya jata hai taakay bias ki iqdaam ko roka ja sakay.
- Privacy aur security: Hassas data ki hifazat karna aur models ke ghair janibdari istemal ko roknay.
- Job displacement: Rozgaar par automation ke mumkina asraat ki nishandahi karna.
- Ghalat maloomat aur manipulation: Jhooti ya ghalat maloomat tayyar karne ke liye models ko istemal karne se roknay.
Companies jaisay SK Telecom ke liye yeh zaroori hai ke woh in akhlaqi tarjeehat ko sargrami se address karein aur apne language models ko zimma dar aur akhlaqi tareeqay se develop aur deploy karein. Is mein bias ko roknay, privacy ki hifazat karne aur shaffafiat ko farogh daine ke liye safeguards implement karna shaamil hai.
Nateeja
SK Telecom ki taraf se khamoshi se A.X 4.0 ka unveal karna Korean zabaan ke optimized barri language models ki development mein aik ahem qadam aagay hai. Performance, efficiency aur real-world applications par is ki tawajjah ke sath, A.X 4.0 mein Korean maeeshat aur society mein aik qadar mandana contribution karne ki salahiyat hai. Jaisay ke SKT apne language models ko develop aur refine karta rehta hai, yeh zaroori hoga ke woh akhlaqi tarjeehat ko address karein aur yaqeen dilaaye ke un powerful technologies ko sab ke faiday ke liye istemal kiya jaye.