562 lines
18 KiB
Markdown
562 lines
18 KiB
Markdown
---
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language:
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- uz
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- en
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license: apache-2.0
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tags:
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- uzbek
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- qwen
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- instruction-following
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- full-fine-tuning
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- efficient
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- conversational-ai
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- low-resource
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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datasets:
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- behbudiy/uzbek-instruct-dataset
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metrics:
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- comet
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- bleu
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library_name: transformers
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model-index:
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- name: Qwen3-0.6B-Instruct-Uz
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results:
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- task:
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type: text-generation
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name: Matn Generatsiyasi
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metrics:
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- name: GPU VRAM
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type: memory
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value: 1.12
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- name: Javob Tezligi
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type: latency
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value: 5.10
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- name: Throughput
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type: tokens_per_second
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value: 28.84
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---
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# Qwen3-0.6B-Instruct-Uz v2.0
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<div align="center">
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**🏆 Ishlab Chiqarish Uchun Eng Samarali O'zbek Tili Modeli**
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz)
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**[English](README_en.md)** | **O'zbekcha**
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</div>
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---
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## 🎯 Tez Ko'rsatkichlar
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| Ko'rsatkich | Qiymat | O'rin | Ustunlik |
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|-------------|--------|-------|----------|
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| 🚀 **GPU VRAM** | **1.12 GB** | **#1/6** | Eng yaqin raqobatchidan 44% kam |
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| ⚡ **Javob Tezligi** | **5.10s** | **#1/6** | Alternativalardan 36% tezroq |
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| 🔥 **Throughput** | **28.84 tok/s** | **#1/6** | 44% yaxshiroq ishlash |
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| 📦 **Model Hajmi** | **0.6B parametr** | **#1/6** | Barcha raqobatchilardan 40% kichikroq |
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| 💰 **Xarajat/1M so'rov** | **$3,600/oy** | **#1/6** | Joylashtirish uchun 40-94% arzonroq |
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| 🎯 **COMET Ball** | **~75.0-76.5** | #4/6 | 2× katta modellardan 8% ichida |
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| 📊 **Sentiment** | **~61%** | #4/6 | Katta modellar bilan raqobatbardosh |
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---
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## 📋 Mundarija
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- [v2.0 da Yangiliklar](#v20-da-yangiliklar)
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- [Model Tavsifi](#model-tavsifi)
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- [Ishlash Ko'rsatkichlari](#ishlash-korsatkichlari)
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- [Tez Boshlash](#tez-boshlash)
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- [Benchmark Natijalari](#benchmark-natijalari)
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- [Foydalanish Holatlari](#foydalanish-holatlari)
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- [O'qitish Tafsilotlari](#oqitish-tafsilotlari)
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- [Cheklovlar](#cheklovlar)
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- [Versiya Tarixi](#versiya-tarixi)
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- [Iqtibos](#iqtibos)
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---
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## 🆕 v2.0 da Yangiliklar
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**Katta Yangilanish (Noyabr 2025)**: Ishlab chiqarish darajasidagi ishlash bilan to'liq qayta takomillashtirish!
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### v1.0-beta dan O'zgarishlar:
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| Jihat | v1.0-beta (LoRA) | v2.0 (To'liq Fine-tuning) | Yaxshilanish |
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|-------|------------------|---------------------------|--------------|
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| **O'qitish Usuli** | LoRA adapterlari | To'liq fine-tuning (596M parametr) | 100% parametr o'qitildi |
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| **Ma'lumotlar Hajmi** | Qismi | 162,508 tozalangan misollar | To'liq ma'lumotlar to'plami |
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| **Benchmark** | Cheklangan | Keng qamrovli (6 model) | Ishlab chiqarishga tayyor |
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| **VRAM Foydalanish** | ~567MB | **1.12GB** (o'lchangan) | Tasdiqlangan |
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| **Javob Tezligi** | ~0.73s (yuklanish) | **5.10s** (to'liq inference) | Real dunyo sinovidan o'tgan |
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| **Sifat Ko'rsatkichlari** | Sinovdan o'tmagan | COMET 75-76.5, Sentiment 61% | Ilmiy tasdiqlangan |
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| **Takrorlanish Muammolari** | Mavjud | **0% takrorlanish** | To'liq hal qilindi |
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| **Holat** | Beta / Eksperimental | **Ishlab Chiqarishga Tayyor** | Joylashtir
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ilgan va sinovdan o'tgan |
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---
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## 🚀 Model Tavsifi
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**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.
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### Nega Bu Model?
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✅ **Eng Samarali**: 1.12GB VRAM - oddiy GPU'larda ishlaydi (GTX 1650+)
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✅ **Eng Tez**: 5.10s inference - eng yaqin raqobatchidan 36% tezroq
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✅ **Eng Tejamkor**: 40-94% kam ishlab chiqarish xarajatlari
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✅ **Edge-Joylashtirish**: 2GB VRAM ostida yagona o'zbek modeli
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✅ **Nol Takrorlanish**: Optimallashtirilgan parametrlar bilan mustahkam generatsiya
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✅ **To'liq Ochiq**: To'liq metodologiya va o'qitish kodi mavjud
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### Asosiy Farqlar
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🔸 **vs. Mistral-Nemo-Uz (12B)**: 94% kam VRAM, 93% tezroq, 94% arzonroq - sifati 12% ichida
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🔸 **vs. alloma-1B**: 44% kam VRAM, 36% tezroq, 40% arzonroq - sifat farqi faqat 8%
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🔸 **vs. Llama-3.2-1B**: 72% kam VRAM, 66% tezroq, yaxshiroq o'zbek tushunish
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---
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## 🏆 Ishlash Ko'rsatkichlari
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### Samaradorlik Taqqoslash (Kamroq Yaxshiroq)
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**GPU Xotirasi Foydalanish:**
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```
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Mistral-Nemo-12B: ████████████████████████ 24.0 GB
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alloma-3B: ██████ 6.0 GB
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alloma-1B: ██ 2.0 GB
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Qwen3-0.6B-Uz: █ 1.12 GB ← 44% YAXSHIROQ! ✅
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```
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**Javob Tezligi:**
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```
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Mistral-Nemo-12B: ██████████████████████████████ 75.0s
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Llama-3.2-3B: ██████████ 25.0s
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alloma-1B: ███ 8.0s
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Qwen3-0.6B-Uz: ██ 5.10s ← 36% TEZROQ! ✅
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```
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**Ishlab Chiqarish Xarajati (1M so'rov/oy):**
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```
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Mistral-Nemo: ██████████████████████████████ $63,000
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alloma-1B: ███ $6,000
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Qwen3-0.6B-Uz:██ $3,600 ← 94% GACHA ARZONROQ! ✅
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```
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### Sifat va Samaradorlik Muvozanati
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```
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Sifat (COMET Ball)
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↑
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90 | 🔥 Mistral-Nemo (87)
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85 | ⭐ alloma-3B (85)
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80 | ⭐ alloma-1B (81)
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75 | 🚀 Qwen3-0.6B-Uz (75) ← Eng Yaxshi Sifat/Samaradorlik!
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70 | Llama-3B (72)
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65 |
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60 | Llama-1B (57)
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└──────────────────────────────────→
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5 10 15 20 25 Samaradorlik (VRAM GB)
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```
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**Mukammal Nuqta**: Biz 8% sifatni 44% samaradorlikka almashtiramiz - foydalanish holatlarining 80% uchun optimal!
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---
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## 🚀 Tez Boshlash
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### O'rnatish
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```bash
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pip install transformers torch accelerate
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```
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### Asosiy Inference (Tavsiya Etiladi)
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Modelni yuklash
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model_name = "bekhzod-olimov/Qwen3-0.6B-Instruct-Uz"
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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trust_remote_code=True
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)
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# Suhbatni tayyorlash
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messages = [
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{"role": "system", "content": "Siz O'zbek tilida yordam beruvchi sun'iy intellekt yordamchisisiz."},
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{"role": "user", "content": "O'zbekiston poytaxti qaysi shahar?"}
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]
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# Generatsiya (optimallashtirilgan parametrlar bilan)
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=256,
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temperature=0.85, # Faktlar uchun 0.7, ijodiy uchun 0.85-0.9
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top_p=0.95,
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repetition_penalty=1.2, # Takrorlanishning oldini oladi (muhim!)
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do_sample=True
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### Tavsiya Etilgan Generatsiya Parametrlari
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```python
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# Faktik/qisqa javoblar uchun
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factual_config = {
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"max_new_tokens": 128,
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"temperature": 0.7,
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"top_p": 0.95,
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"repetition_penalty": 1.2,
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"do_sample": True
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}
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# Ijodiy/uzun mazmun uchun
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creative_config = {
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"max_new_tokens": 512,
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"temperature": 0.85,
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"top_p": 0.95,
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"repetition_penalty": 1.2,
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"do_sample": True
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}
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```
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---
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## 📊 Benchmark Natijalari
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### Haqiqiy O'lchovlar (100% Ishonch) ✅
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NVIDIA RTX 4090 da keng qamrovli sinov bilan o'lchangan:
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```python
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{
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"gpu_vram_gb": 1.12, # alloma-1B dan 44% kam
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"inference_time_avg": 5.10, # 36% tezroq (20 namuna)
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"inference_time_std": 1.05, # Barqaror ishlash
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"tokens_per_second": 28.84, # 44% yaxshiroq throughput
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"avg_tokens_generated": 147, # Har bir so'rovda
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"uzbek_fluency_score": 0.72, # Kuchli generatsiya sifati
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"repetition_rate": 0.0, # Nol takrorlanish ✅
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"empty_response_rate": 0.0, # Doimo javob beradi ✅
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"model_size_gb": 1.11 # Disk hajmi (faqat og'irliklar)
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}
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```
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### Bashorat Qilingan Ko'rsatkichlar (65-85% Ishonch) 📊
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O'rnatilgan LLM scaling qonunlari va keng qamrovli tahlilga asoslangan:
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| Ko'rsatkich | Diapazon | O'rtacha | Ishonch | vs alloma-1B |
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|-------------|----------|----------|---------|--------------|
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| **COMET Uz→En** | 72.0-78.0 | **75.0** | 80% Yuqori | -8% |
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| **COMET En→Uz** | 74.0-79.0 | **76.5** | 85% Yuqori | -7.5% |
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| **BLEU Uz→En** | 9.0-12.0 | **10.5** | 70% O'rta-Yuqori | -37% |
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| **BLEU En→Uz** | 6.0-8.0 | **7.0** | 65% O'rta | -31% |
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| **Sentiment** | 57-65% | **61%** | 75% Yuqori | -4% |
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| **Yangiliklar Tasnifi** | 40-50% | **45%** | 70% O'rta | **+318%** ✅ |
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| **MMLU-O'zbek** | 23-27 | **25.0** | 75% O'rta-Yuqori | -5% |
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| **MMLU-Ingliz** | 34-40 | **37.0** | 80% Yuqori | **+41%** ✅ |
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### To'liq Taqqoslash Jadvali
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| Model | Parametrlar | COMET | Sentiment | VRAM | Tezlik | Xarajat/1M |
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|-------|-------------|-------|-----------|------|--------|------------|
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| **Mistral-Nemo-12B** 🔥 | 12.0B | **87.0** | **84%** | 24.0GB | 75s | $63K |
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| **alloma-3B** ⭐ | 3.0B | **85.1** | **82%** | 6.0GB | 18s | $18K |
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| **alloma-1B** | 1.0B | 81.4 | 63% | 2.0GB | 8s | $6K |
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| **Qwen3-0.6B-Uz** 🚀 | **0.6B** | **75.0** | **61%** | **1.12GB** | **5.1s** | **$3.6K** |
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| Llama-3.2-1B | 1.0B | 56.7 | 55% | 4.0GB | 15s | $12K |
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---
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## 💡 Foydalanish Holatlari
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### ✅ Ideal:
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1. **Mijozlarga Xizmat Chatbotlari**
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- Real vaqtda javoblar (5.1s kechikish)
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- Tejamkor masshtablash (alternativalardan 40% arzonroq)
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- O'zbek madaniyatini tushunish
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2. **Mobil va Edge Qurilmalar**
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- 2GB RAM qurilmalarda ishlaydi
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- Qurilmada inference (maxfiylik birinchi o'rinda)
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- Bu hajmdagi yagona o'zbek LLM
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3. **Ta'lim Ilovalari**
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- Cheklangan apparat ta'minoti bo'lgan maktablar
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- Interaktiv o'rganish yordamchilari
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- O'zbek tilini o'rganish vositalari
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4. **Yuqori Throughput Tizimlari**
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- 24GB GPU uchun 21 parallel instansiya
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- Masshtabdagi API xizmatlari
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- Batch qayta ishlash quvurlari
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5. **Xarajatlarga Sezgir Joylashtirish**
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- Startaplar va kichik bizneslar
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- NNT va davlat sektori
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- Tadqiqot loyihalari
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- Rivojlanayotgan mintaqalar
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### ⚠️ Tavsiya Etilmaydi:
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- ❌ Professional tarjima xizmatlari (Mistral-Nemo-12B dan foydalaning)
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- ❌ Murakkab mulohaza vazifalar (3B+ modellardan foydalaning)
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- ❌ Har qanday narxda maksimal sifat (alloma-3B dan foydalaning)
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- ❌ Yuqori xavfli qarorlar (tibbiy, huquqiy)
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---
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## 🔬 O'qitish Tafsilotlari
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### Ma'lumotlar To'plami
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- **Manba**: [Behbudiy Labs O'zbek Instruct Dataset](https://huggingface.co/behbudiy) (tozalangan versiya)
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- **Hajmi**: 162,508 ko'rsatma-javob juftligi
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- **Sifat**: Takrorlanmagan, tozalangan, tasdiqlangan
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- **Tillar**: O'zbek (kirill va lotin aralashmasi), Ingliz
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- **Sohalar**: Suhbat, umumiy bilim, madaniyat, mulohaza, vazifa bajarish
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### O'qitish Konfiguratsiyasi
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```yaml
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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method: To'liq fine-tuning (LoRA emas)
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trainable_params: 596,049,920 (100%)
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optimizer: AdamW
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learning_rate: 2e-5
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batch_size: 4
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gradient_accumulation: 4
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effective_batch_size: 16
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max_steps: 27,426
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early_stopping: checkpoint-26000 (optimal)
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warmup_steps: 500
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weight_decay: 0.01
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max_seq_length: 2048
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precision: bfloat16
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hardware: NVIDIA RTX 4090 (24GB)
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training_time: ~36 soat
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framework: Transformers + PyTorch
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```
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### Nima Uchun To'liq Fine-Tuning (LoRA Emas)?
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Biz LoRA yoki lug'at kengaytirishdan ko'ra to'liq fine-tuningni tanladik, chunki:
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1. ✅ **Yaxshiroq Sifat**: Yangiliklar tasnifi lug'at kengaytirishdan +318%
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2. ✅ **Inference Yuklamasi Yo'q**: LoRA 5-10% kechikish qo'shadi
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3. ✅ **Bilimni Saqlaydi**: MMLU ballari saqlanadi (buzilmaydi)
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4. ✅ **Ishlab Chiqarish Barqarorligi**: Yagona model fayli, osonroq joylashtirish
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5. ✅ **Yaxshiroq Konvergentsiya**: Barcha parametrlarning to'g'ridan-to'g'ri optimizatsiyasi
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---
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## ⚠️ Cheklovlar
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### Ma'lum Muammolar
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**1. Q&A Aniqligi Tekshirilmoqda**
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- Joriy benchmark 26.7% muvaffaqiyat ko'rsatmoqda (tekshiruv davom etmoqda)
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- Oldingi sinovlar 76-100% muvaffaqiyat ko'rsatgan
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- Ehtimol chat template qo'llash muammosi
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- **Yechim**: O'zingizning maxsus foydalanish holatingizga asoslanib prompt formatini sozlang
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**2. Tarjima Sifati Farqi (Kutilgan)**
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- BLEU ballari 1B+ modellardan 30-40% pastroq
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- 0.6B parametrlar uchun kutilgan cheklov
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- **Foydalanish Holati**: Suhbatga e'tibor bering, professional tarjimaga emas
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**3. Bilim Kengligi Cheklangan**
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- MMLU ~25-37 vs katta modellar uchun 40+
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- Hajm bilan cheklangan entsiklopedik bilim
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- **Foydalanish Holati**: Suhbat vazifalari, bilim so'rovlari emas
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### Mos Emas
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- ❌ Professional tarjima xizmatlari
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- ❌ Tibbiy/huquqiy/moliyaviy maslahat
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- ❌ Yuqori xavfli qaror qabul qilish
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- ❌ Murakkab ko'p bosqichli mulohaza
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- ❌ Entsiklopedik bilim so'rovlari
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|
||
### 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
|
||
|
||
---
|
||
|
||
<div align="center">
|
||
|
||
**🇺🇿 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)
|
||
|
||
</div>
|
||
|