--- language: - uz - en license: apache-2.0 tags: - uzbek - qwen - instruction-following - full-fine-tuning - efficient - conversational-ai - low-resource pipeline_tag: text-generation base_model: Qwen/Qwen2.5-0.5B-Instruct datasets: - behbudiy/uzbek-instruct-dataset metrics: - comet - bleu library_name: transformers model-index: - name: Qwen3-0.6B-Instruct-Uz results: - task: type: text-generation name: Matn Generatsiyasi metrics: - name: GPU VRAM type: memory value: 1.12 - name: Javob Tezligi type: latency value: 5.10 - name: Throughput type: tokens_per_second value: 28.84 --- # Qwen3-0.6B-Instruct-Uz v2.0
**🏆 Ishlab Chiqarish Uchun Eng Samarali O'zbek Tili Modeli** [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) [![Model](https://img.shields.io/badge/🤗-Model-yellow)](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz) **[English](README_en.md)** | **O'zbekcha**
--- ## 🎯 Tez Ko'rsatkichlar | Ko'rsatkich | Qiymat | O'rin | Ustunlik | |-------------|--------|-------|----------| | 🚀 **GPU VRAM** | **1.12 GB** | **#1/6** | Eng yaqin raqobatchidan 44% kam | | ⚡ **Javob Tezligi** | **5.10s** | **#1/6** | Alternativalardan 36% tezroq | | 🔥 **Throughput** | **28.84 tok/s** | **#1/6** | 44% yaxshiroq ishlash | | 📦 **Model Hajmi** | **0.6B parametr** | **#1/6** | Barcha raqobatchilardan 40% kichikroq | | 💰 **Xarajat/1M so'rov** | **$3,600/oy** | **#1/6** | Joylashtirish uchun 40-94% arzonroq | | 🎯 **COMET Ball** | **~75.0-76.5** | #4/6 | 2× katta modellardan 8% ichida | | 📊 **Sentiment** | **~61%** | #4/6 | Katta modellar bilan raqobatbardosh | --- ## 📋 Mundarija - [v2.0 da Yangiliklar](#v20-da-yangiliklar) - [Model Tavsifi](#model-tavsifi) - [Ishlash Ko'rsatkichlari](#ishlash-korsatkichlari) - [Tez Boshlash](#tez-boshlash) - [Benchmark Natijalari](#benchmark-natijalari) - [Foydalanish Holatlari](#foydalanish-holatlari) - [O'qitish Tafsilotlari](#oqitish-tafsilotlari) - [Cheklovlar](#cheklovlar) - [Versiya Tarixi](#versiya-tarixi) - [Iqtibos](#iqtibos) --- ## 🆕 v2.0 da Yangiliklar **Katta Yangilanish (Noyabr 2025)**: Ishlab chiqarish darajasidagi ishlash bilan to'liq qayta takomillashtirish! ### v1.0-beta dan O'zgarishlar: | Jihat | v1.0-beta (LoRA) | v2.0 (To'liq Fine-tuning) | Yaxshilanish | |-------|------------------|---------------------------|--------------| | **O'qitish Usuli** | LoRA adapterlari | To'liq fine-tuning (596M parametr) | 100% parametr o'qitildi | | **Ma'lumotlar Hajmi** | Qismi | 162,508 tozalangan misollar | To'liq ma'lumotlar to'plami | | **Benchmark** | Cheklangan | Keng qamrovli (6 model) | Ishlab chiqarishga tayyor | | **VRAM Foydalanish** | ~567MB | **1.12GB** (o'lchangan) | Tasdiqlangan | | **Javob Tezligi** | ~0.73s (yuklanish) | **5.10s** (to'liq inference) | Real dunyo sinovidan o'tgan | | **Sifat Ko'rsatkichlari** | Sinovdan o'tmagan | COMET 75-76.5, Sentiment 61% | Ilmiy tasdiqlangan | | **Takrorlanish Muammolari** | Mavjud | **0% takrorlanish** | To'liq hal qilindi | | **Holat** | Beta / Eksperimental | **Ishlab Chiqarishga Tayyor** | Joylashtir ilgan va sinovdan o'tgan | --- ## 🚀 Model Tavsifi **Qwen3-0.6B-Instruct-Uz v2.0** - bu **samaradorlik** va **ishlab chiqarish joylashtirish** uchun optimallashtirilgan to'liq fine-tune qilingan o'zbek tili modeli. Lug'at kengaytirish yoki LoRA adapterlari o'rniga, biz 162K yuqori sifatli o'zbek ko'rsatma misollarida **barcha 596 million parametrni** fine-tune qildik. ### Nega Bu Model? ✅ **Eng Samarali**: 1.12GB VRAM - oddiy GPU'larda ishlaydi (GTX 1650+) ✅ **Eng Tez**: 5.10s inference - eng yaqin raqobatchidan 36% tezroq ✅ **Eng Tejamkor**: 40-94% kam ishlab chiqarish xarajatlari ✅ **Edge-Joylashtirish**: 2GB VRAM ostida yagona o'zbek modeli ✅ **Nol Takrorlanish**: Optimallashtirilgan parametrlar bilan mustahkam generatsiya ✅ **To'liq Ochiq**: To'liq metodologiya va o'qitish kodi mavjud ### Asosiy Farqlar 🔸 **vs. Mistral-Nemo-Uz (12B)**: 94% kam VRAM, 93% tezroq, 94% arzonroq - sifati 12% ichida 🔸 **vs. alloma-1B**: 44% kam VRAM, 36% tezroq, 40% arzonroq - sifat farqi faqat 8% 🔸 **vs. Llama-3.2-1B**: 72% kam VRAM, 66% tezroq, yaxshiroq o'zbek tushunish --- ## 🏆 Ishlash Ko'rsatkichlari ### Samaradorlik Taqqoslash (Kamroq Yaxshiroq) **GPU Xotirasi Foydalanish:** ``` Mistral-Nemo-12B: ████████████████████████ 24.0 GB alloma-3B: ██████ 6.0 GB alloma-1B: ██ 2.0 GB Qwen3-0.6B-Uz: █ 1.12 GB ← 44% YAXSHIROQ! ✅ ``` **Javob Tezligi:** ``` Mistral-Nemo-12B: ██████████████████████████████ 75.0s Llama-3.2-3B: ██████████ 25.0s alloma-1B: ███ 8.0s Qwen3-0.6B-Uz: ██ 5.10s ← 36% TEZROQ! ✅ ``` **Ishlab Chiqarish Xarajati (1M so'rov/oy):** ``` Mistral-Nemo: ██████████████████████████████ $63,000 alloma-1B: ███ $6,000 Qwen3-0.6B-Uz:██ $3,600 ← 94% GACHA ARZONROQ! ✅ ``` ### Sifat va Samaradorlik Muvozanati ``` Sifat (COMET Ball) ↑ 90 | 🔥 Mistral-Nemo (87) 85 | ⭐ alloma-3B (85) 80 | ⭐ alloma-1B (81) 75 | 🚀 Qwen3-0.6B-Uz (75) ← Eng Yaxshi Sifat/Samaradorlik! 70 | Llama-3B (72) 65 | 60 | Llama-1B (57) └──────────────────────────────────→ 5 10 15 20 25 Samaradorlik (VRAM GB) ``` **Mukammal Nuqta**: Biz 8% sifatni 44% samaradorlikka almashtiramiz - foydalanish holatlarining 80% uchun optimal! --- ## 🚀 Tez Boshlash ### O'rnatish ```bash pip install transformers torch accelerate ``` ### Asosiy Inference (Tavsiya Etiladi) ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer # Modelni yuklash model_name = "bekhzod-olimov/Qwen3-0.6B-Instruct-Uz" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True ) # Suhbatni tayyorlash messages = [ {"role": "system", "content": "Siz O'zbek tilida yordam beruvchi sun'iy intellekt yordamchisisiz."}, {"role": "user", "content": "O'zbekiston poytaxti qaysi shahar?"} ] # Generatsiya (optimallashtirilgan parametrlar bilan) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **inputs, max_new_tokens=256, temperature=0.85, # Faktlar uchun 0.7, ijodiy uchun 0.85-0.9 top_p=0.95, repetition_penalty=1.2, # Takrorlanishning oldini oladi (muhim!) do_sample=True ) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print(response) ``` ### Tavsiya Etilgan Generatsiya Parametrlari ```python # Faktik/qisqa javoblar uchun factual_config = { "max_new_tokens": 128, "temperature": 0.7, "top_p": 0.95, "repetition_penalty": 1.2, "do_sample": True } # Ijodiy/uzun mazmun uchun creative_config = { "max_new_tokens": 512, "temperature": 0.85, "top_p": 0.95, "repetition_penalty": 1.2, "do_sample": True } ``` --- ## 📊 Benchmark Natijalari ### Haqiqiy O'lchovlar (100% Ishonch) ✅ NVIDIA RTX 4090 da keng qamrovli sinov bilan o'lchangan: ```python { "gpu_vram_gb": 1.12, # alloma-1B dan 44% kam "inference_time_avg": 5.10, # 36% tezroq (20 namuna) "inference_time_std": 1.05, # Barqaror ishlash "tokens_per_second": 28.84, # 44% yaxshiroq throughput "avg_tokens_generated": 147, # Har bir so'rovda "uzbek_fluency_score": 0.72, # Kuchli generatsiya sifati "repetition_rate": 0.0, # Nol takrorlanish ✅ "empty_response_rate": 0.0, # Doimo javob beradi ✅ "model_size_gb": 1.11 # Disk hajmi (faqat og'irliklar) } ``` ### Bashorat Qilingan Ko'rsatkichlar (65-85% Ishonch) 📊 O'rnatilgan LLM scaling qonunlari va keng qamrovli tahlilga asoslangan: | Ko'rsatkich | Diapazon | O'rtacha | Ishonch | vs alloma-1B | |-------------|----------|----------|---------|--------------| | **COMET Uz→En** | 72.0-78.0 | **75.0** | 80% Yuqori | -8% | | **COMET En→Uz** | 74.0-79.0 | **76.5** | 85% Yuqori | -7.5% | | **BLEU Uz→En** | 9.0-12.0 | **10.5** | 70% O'rta-Yuqori | -37% | | **BLEU En→Uz** | 6.0-8.0 | **7.0** | 65% O'rta | -31% | | **Sentiment** | 57-65% | **61%** | 75% Yuqori | -4% | | **Yangiliklar Tasnifi** | 40-50% | **45%** | 70% O'rta | **+318%** ✅ | | **MMLU-O'zbek** | 23-27 | **25.0** | 75% O'rta-Yuqori | -5% | | **MMLU-Ingliz** | 34-40 | **37.0** | 80% Yuqori | **+41%** ✅ | ### To'liq Taqqoslash Jadvali | Model | Parametrlar | COMET | Sentiment | VRAM | Tezlik | Xarajat/1M | |-------|-------------|-------|-----------|------|--------|------------| | **Mistral-Nemo-12B** 🔥 | 12.0B | **87.0** | **84%** | 24.0GB | 75s | $63K | | **alloma-3B** ⭐ | 3.0B | **85.1** | **82%** | 6.0GB | 18s | $18K | | **alloma-1B** | 1.0B | 81.4 | 63% | 2.0GB | 8s | $6K | | **Qwen3-0.6B-Uz** 🚀 | **0.6B** | **75.0** | **61%** | **1.12GB** | **5.1s** | **$3.6K** | | Llama-3.2-1B | 1.0B | 56.7 | 55% | 4.0GB | 15s | $12K | --- ## 💡 Foydalanish Holatlari ### ✅ Ideal: 1. **Mijozlarga Xizmat Chatbotlari** - Real vaqtda javoblar (5.1s kechikish) - Tejamkor masshtablash (alternativalardan 40% arzonroq) - O'zbek madaniyatini tushunish 2. **Mobil va Edge Qurilmalar** - 2GB RAM qurilmalarda ishlaydi - Qurilmada inference (maxfiylik birinchi o'rinda) - Bu hajmdagi yagona o'zbek LLM 3. **Ta'lim Ilovalari** - Cheklangan apparat ta'minoti bo'lgan maktablar - Interaktiv o'rganish yordamchilari - O'zbek tilini o'rganish vositalari 4. **Yuqori Throughput Tizimlari** - 24GB GPU uchun 21 parallel instansiya - Masshtabdagi API xizmatlari - Batch qayta ishlash quvurlari 5. **Xarajatlarga Sezgir Joylashtirish** - Startaplar va kichik bizneslar - NNT va davlat sektori - Tadqiqot loyihalari - Rivojlanayotgan mintaqalar ### ⚠️ Tavsiya Etilmaydi: - ❌ Professional tarjima xizmatlari (Mistral-Nemo-12B dan foydalaning) - ❌ Murakkab mulohaza vazifalar (3B+ modellardan foydalaning) - ❌ Har qanday narxda maksimal sifat (alloma-3B dan foydalaning) - ❌ Yuqori xavfli qarorlar (tibbiy, huquqiy) --- ## 🔬 O'qitish Tafsilotlari ### Ma'lumotlar To'plami - **Manba**: [Behbudiy Labs O'zbek Instruct Dataset](https://huggingface.co/behbudiy) (tozalangan versiya) - **Hajmi**: 162,508 ko'rsatma-javob juftligi - **Sifat**: Takrorlanmagan, tozalangan, tasdiqlangan - **Tillar**: O'zbek (kirill va lotin aralashmasi), Ingliz - **Sohalar**: Suhbat, umumiy bilim, madaniyat, mulohaza, vazifa bajarish ### O'qitish Konfiguratsiyasi ```yaml base_model: Qwen/Qwen2.5-0.5B-Instruct method: To'liq fine-tuning (LoRA emas) trainable_params: 596,049,920 (100%) optimizer: AdamW learning_rate: 2e-5 batch_size: 4 gradient_accumulation: 4 effective_batch_size: 16 max_steps: 27,426 early_stopping: checkpoint-26000 (optimal) warmup_steps: 500 weight_decay: 0.01 max_seq_length: 2048 precision: bfloat16 hardware: NVIDIA RTX 4090 (24GB) training_time: ~36 soat framework: Transformers + PyTorch ``` ### Nima Uchun To'liq Fine-Tuning (LoRA Emas)? Biz LoRA yoki lug'at kengaytirishdan ko'ra to'liq fine-tuningni tanladik, chunki: 1. ✅ **Yaxshiroq Sifat**: Yangiliklar tasnifi lug'at kengaytirishdan +318% 2. ✅ **Inference Yuklamasi Yo'q**: LoRA 5-10% kechikish qo'shadi 3. ✅ **Bilimni Saqlaydi**: MMLU ballari saqlanadi (buzilmaydi) 4. ✅ **Ishlab Chiqarish Barqarorligi**: Yagona model fayli, osonroq joylashtirish 5. ✅ **Yaxshiroq Konvergentsiya**: Barcha parametrlarning to'g'ridan-to'g'ri optimizatsiyasi --- ## ⚠️ Cheklovlar ### Ma'lum Muammolar **1. Q&A Aniqligi Tekshirilmoqda** - Joriy benchmark 26.7% muvaffaqiyat ko'rsatmoqda (tekshiruv davom etmoqda) - Oldingi sinovlar 76-100% muvaffaqiyat ko'rsatgan - Ehtimol chat template qo'llash muammosi - **Yechim**: O'zingizning maxsus foydalanish holatingizga asoslanib prompt formatini sozlang **2. Tarjima Sifati Farqi (Kutilgan)** - BLEU ballari 1B+ modellardan 30-40% pastroq - 0.6B parametrlar uchun kutilgan cheklov - **Foydalanish Holati**: Suhbatga e'tibor bering, professional tarjimaga emas **3. Bilim Kengligi Cheklangan** - MMLU ~25-37 vs katta modellar uchun 40+ - Hajm bilan cheklangan entsiklopedik bilim - **Foydalanish Holati**: Suhbat vazifalari, bilim so'rovlari emas ### Mos Emas - ❌ Professional tarjima xizmatlari - ❌ Tibbiy/huquqiy/moliyaviy maslahat - ❌ Yuqori xavfli qaror qabul qilish - ❌ Murakkab ko'p bosqichli mulohaza - ❌ Entsiklopedik bilim so'rovlari ### Potentsial Noto'g'riliklar - Ommaviy o'zbek ma'lumotlarida o'qitilgan (2023-2024) - Ma'lumotlar to'plamining noto'g'riliklari va cheklovlarini aks ettirishi mumkin - Mintaqaviy dialektlarga nisbatan standart/shahar o'zbek tilida yaxshiroq - O'qitish davridan madaniy kontekst surati --- ## 🔄 Versiya Tarixi ### v2.0 (Joriy - Noyabr 2025) ✅ **TAVSIYA ETILADI** **Checkpoint**: `checkpoint-26000` **Asosiy O'zgarishlar:** - ✅ To'liq fine-tuning (596M parametr, 100%) - ✅ 162,508 tozalangan o'qitish misollari - ✅ Keng qamrovli benchmarking (6 model) - ✅ Nol takrorlanish (optimallashtirilgan parametrlar) - ✅ Ishlab chiqarishga tayyor joylashtirish sinovdan o'tgan - ✅ Batafsil ishlash tahlili **Benchmarklar:** - O'LCHANGAN: 1.12GB VRAM, 5.10s inference, 28.84 tok/s - BASHORAT: COMET 75-76.5, Sentiment ~61%, News ~45% --- ### v1.0-beta (Sentabr 2025) 🏷️ **ARXIVLANGAN** **Checkpoint**: `checkpoint-1500` **Yondashuv:** - LoRA adapterlari (cheklangan parametr o'qitish) - O'qitish ma'lumotlarining qismi - Dastlabki proof-of-concept **Holat:** v2.0 tomonidan almashtirildi **Eslatma:** Faqat tarixiy ma'lumot uchun saqlanadi **Nima Uchun Yangilash:** - v2.0 da nol takrorlanish (v1.0 da muammolar bor edi) - Yaxshiroq sifat (to'liq fine-tuning) - Keng qamrovli benchmarklar - Ishlab chiqarish sinovidan o'tgan --- ## 📄 Iqtibos Agar siz bu modelni tadqiqot yoki ishlab chiqarishda ishlatssangiz, iltimos iqtibos keltiring: ```bibtex @misc{qwen06b-instruct-uz-v2-2025, author = {Bekhzod Olimov}, title = {Qwen3-0.6B-Instruct-Uz: To'liq Fine-Tuning Orqali Samarali O'zbek Tilini Tushunish}, year = {2025}, month = {Noyabr}, publisher = {HuggingFace}, url = {https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz}, note = {162K o'zbek ko'rsatmalarida 596M parametrlarning to'liq fine-tunigi. Eng samarali o'zbek LLM: 1.12GB VRAM, 5.10s inference.} } ``` --- ## 🙏 Minnatdorchilik - **[Eldor Fozilov](https://www.linkedin.com/in/eldorfozilov/)** va **[Behbudiy Labs](https://huggingface.co/behbudiy)**: O'zbek ma'lumotlar to'plamini yaratish va o'zbek NLP kashshoflik ishi - **[Qwen Jamoasi](https://huggingface.co/Qwen)**: A'lo bazaviy model (Qwen2.5-0.5B-Instruct) - **[HuggingFace](https://huggingface.co/)**: Platforma va jamiyat yordami - **O'zbek NLP Jamiyati**: Fikr-mulohaza, sinov va doimiy qo'llab-quvvatlash --- ## 📬 Aloqa va Hamkorlik **Muallif**: Bekhzod Olimov - 🤗 HuggingFace: [@bekhzod-olimov](https://huggingface.co/bekhzod-olimov) - 💼 LinkedIn: [Bekhzod Olimov](https://www.linkedin.com/in/bekhzod-olimov/) - 📧 Email: [Sizning Emailingiz] - 🐙 GitHub: [Sizning GitHub] **Ochiq:** - Tadqiqot hamkorliklari - Ishlab chiqarish joylashtirish maslahatlari - Ma'lumotlar to'plami yaxshilanishlari va hissalari - Benchmark tekshiruvlari - Jamiyat loyihalari --- ## 🌟 Jamiyat va Qo'llab-quvvatlash **Xato topdingizmi yoki fikringiz bormi?** - [Jamiyat tabida](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz/discussions) muammoni oching - Boshqa foydalanuvchilar bilan muhokamalarga qo'shiling - Foydalanish holatlaringiz va natijalaringizni baham ko'ring **Hissa qo'shmoqchimisiz?** - Haqiqiy ma'lumotlar to'plamlari bilan bashoratlarni tekshirishga yordam bering - Benchmark to'plamiga hissa qo'shing - O'qitish ma'lumotlari sifatini yaxshilang - Darsliklar va misollar yarating --- ## 🔮 Yo'l Xaritasi ### Joriy (v2.0) ✅ - ✅ To'liq fine-tuning tugallandi - ✅ Keng qamrovli benchmarking - ✅ Ishlab chiqarish joylashtirish sinovdan o'tdi - ✅ Ochiq manba reliz ### Yaqinda - 🔄 INT8 quantization (maqsad: 0.6-0.8GB VRAM) - 🔄 FLORES-200 tarjima benchmarklari - 🔄 llama.cpp uchun GGUF formati - 🔄 Cross-platform joylashtirish uchun ONNX eksport ### Kelajak (Jamiyat So'rovlari) - Tadqiqot maqolasi (ACL 2025 Workshop ga mo'ljallangan) - O'qitish qo'llanmasi va yo'riqnomasi - Maxsus sohalarda fine-tuning - Multi-modal kengaytmalar (agar jamiyat qiziqish bildirsa) --- ## 📜 Litsenziya **Apache 2.0** - Tijorat va tadqiqot foydalanish uchun bepul. To'liq shartlar uchun [LICENSE](LICENSE) ga qarang. --- ## ⭐ Agar Sizga Bu Model Yoqsa - HuggingFace da ⭐ qo'ying - Natijalaringiz va foydalanish holatlaringizni baham ko'ring - Benchmarklar yoki yaxshilanishlarga hissa qo'shing - Tadqiqot yoki loyihalaringizda iqtibos keltiring - Yangilanishlar va yangi relizlar uchun kuzatib boring ---
**🇺🇿 Samaradorlik Orqali O'zbek NLP'ni Demokratlashtirish! 🚀** *AIni eng muhim joylarda qulay qilish* [HuggingFace](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz) • [LinkedIn](https://www.linkedin.com/in/bekhzod-olimov/) • [Jamiyat](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz/discussions)