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Source channel @githubtrending · Post #14909 · Jul 3

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#other#agent#llm#rag Happy-LLM is a free, open-source learning project that helps you deeply understand large language models (LLMs) from basics to advanced training and applications. It teaches you key concepts like NLP, Transformer architecture, pretraining, and how to build and train your own LLaMA2 model step-by-step. You also learn practical skills like fine-tuning and using cutting-edge techniques such as Retrieval-Augmented Generation (RAG) and intelligent agents. This project is ideal if you know some Python and deep learning, and it offers both theory and hands-on code to help you master LLM development and apply it in real-world AI tasks. This can boost your skills and confidence in AI model building and research. https://github.com/datawhalechina/happy-llm

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Ali Ahmad

@AlisherAhmatovich · Post #1189 · 01/09/2025, 04:15 PM

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O'zbekcha STT (Speech-to-Text) uchun benchmarking v1 tayyor! O'zbek tilida nutqni matnga aylantirish bo'yicha dastlabki etalon tizimni yaratdik! Ushbu jarayonda: ✅ Adabiy O'zbek tili ✅ Turli hududiy shevalar ✅ Musiqalar va suhbatlar ✅ Shovqinli audiolar bilan o’zimizni va bozordagi mavjud modellarni sinab ko'rdik. Tez orada natijalarni va benchmarkni e’lon qilamiz, hamda benchmarkni doimiy yangilab borish niyatimiz bor. Benchmark mezonlari: - WER (Word Error Rate) - So’zlar xatolik darajasi. - CER (Character Error Rate) – Harflar xatolik darajasi. Bu nafaqat texnologik rivojlanish, balki O'zbek tilining raqamli muhitda mustahkam o'rin egallashi yo'lida muhim qadamdir. Maqsadimiz – O'zbekcha STT modellarining aniq va sifatli ishlashini ta'minlashdir. Kelajak rejalari: - Test natijalarini batafsil tahlil qilib, modellarni yanada takomillashtirish. O'zbek tili texnologiyalari rivoji uchun birgalikda ishlashda davom etamiz! STT modelimizni @aishaovozbot’da yoki aisha.group’da sinab ko’rishingiz mumkin. #aisha#stt @elzodxon

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