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Source channel @githubtrending · Post #15263 · Nov 2

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#python#deep_learning#inference#llm#nlp#pytorch#transformer Nano-vLLM is a small, fast, and easy-to-understand tool for running large language models offline. It matches the speed of bigger systems like vLLM but uses only about 1,200 lines of clean Python code, making it simple to read and modify. It includes smart features like prefix caching and tensor parallelism to boost performance. You can install it easily and run models like Qwen3-0.6B on your own GPU. This tool is great if you want fast, efficient AI inference without complex setups, ideal for learning, research, or small deployments on limited hardware. https://github.com/GeeeekExplorer/nano-vllm

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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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