567 lines
17 KiB
Markdown
567 lines
17 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: Text Generation
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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: Inference Time
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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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**🏆 The Most Resource-Efficient Uzbek Language Model for Production Deployment**
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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** | **[O'zbekcha](README_uz.md)**
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</div>
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---
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## 🎯 Quick Performance Summary
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| Metric | Value | Rank | Advantage |
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|--------|-------|------|-----------|
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| 🚀 **GPU VRAM** | **1.12 GB** | **#1/6** | 44% less than closest competitor |
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| ⚡ **Inference Speed** | **5.10s** | **#1/6** | 36% faster than alternatives |
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| 🔥 **Throughput** | **28.84 tok/s** | **#1/6** | 44% better performance |
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| 📦 **Model Size** | **0.6B params** | **#1/6** | 40% smaller than all competitors |
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| 💰 **Cost/1M queries** | **$3,600/mo** | **#1/6** | 40-94% cheaper to deploy |
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| 🎯 **COMET Score** | **~75.0-76.5** | #4/6 | Within 8% of 2× larger models |
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| 📊 **Sentiment** | **~61%** | #4/6 | Competitive with larger models |
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---
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## 📋 Table of Contents
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- [What's New in v2.0](#whats-new-in-v20)
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- [Model Description](#model-description)
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- [Performance Highlights](#performance-highlights)
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- [Quick Start](#quick-start)
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- [Benchmarks](#benchmarks)
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- [Use Cases](#use-cases)
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- [Training Details](#training-details)
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- [Limitations](#limitations)
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- [Version History](#version-history)
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- [Citation](#citation)
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---
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## 🆕 What's New in v2.0
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**Major Update (November 2025)**: Complete reimagining with production-grade performance!
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### Changes from v1.0-beta:
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| Aspect | v1.0-beta (LoRA) | v2.0 (Full Fine-tuning) | Improvement |
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|--------|------------------|-------------------------|-------------|
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| **Training Method** | LoRA adapters | Full fine-tuning (596M params) | 100% params trained |
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| **Dataset Size** | Subset | 162,508 cleaned examples | Complete dataset |
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| **Benchmarking** | Limited | Comprehensive (6 models) | Production-ready |
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| **VRAM Usage** | ~567MB | **1.12GB** (measured) | Verified |
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| **Inference Speed** | ~0.73s (loading) | **5.10s** (full inference) | Real-world tested |
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| **Quality Metrics** | Untested | COMET 75-76.5, Sentiment 61% | Scientifically validated |
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| **Repetition Issues** | Present | **0% repetition rate** | Completely fixed |
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| **Status** | Beta / Experimental | **Production-Ready** | Deployed & tested |
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---
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## 🚀 Model Description
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**Qwen3-0.6B-Instruct-Uz v2.0** is a fully fine-tuned Uzbek language model optimized for **efficiency** and **production deployment**. Unlike vocabulary expansion approaches or LoRA adapters, we fine-tuned **all 596 million parameters** on 162K high-quality Uzbek instruction examples.
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### Why This Model?
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✅ **Most Efficient**: 1.12GB VRAM - runs on consumer GPUs (GTX 1650+)
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✅ **Fastest**: 5.10s inference - 36% faster than closest competitor
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✅ **Most Cost-Effective**: 40-94% lower production costs
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✅ **Edge-Deployable**: Only Uzbek model under 2GB VRAM
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✅ **Zero Repetition**: Robust generation with optimized parameters
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✅ **Fully Open**: Complete methodology and training code available
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### Key Differentiators
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🔸 **vs. Mistral-Nemo-Uz (12B)**: 94% less VRAM, 93% faster, 94% cheaper - same quality within 12%
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🔸 **vs. alloma-1B**: 44% less VRAM, 36% faster, 40% cheaper - quality gap only 8%
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🔸 **vs. Llama-3.2-1B**: 72% less VRAM, 66% faster, better Uzbek understanding
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---
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## 🏆 Performance Highlights
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### Efficiency Comparison (Lower is Better)
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**GPU Memory Usage:**
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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% BETTER! ✅
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```
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**Inference Speed:**
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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% FASTER! ✅
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```
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**Production Cost (1M queries/month):**
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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 ← UP TO 94% CHEAPER! ✅
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```
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### Quality vs Efficiency Tradeoff
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```
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Quality (COMET Score)
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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) ← Best Quality/Efficiency!
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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 Efficiency (VRAM GB)
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```
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**Sweet Spot**: We trade 8% quality for 44% efficiency - optimal for 80% of use cases!
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---
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## 🚀 Quick Start
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### Installation
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```bash
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pip install transformers torch accelerate
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```
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### Basic Inference (Recommended)
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load model
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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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# Prepare conversation
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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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# Generate (with optimized parameters)
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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, # 0.7 for factual, 0.85-0.9 for creative
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top_p=0.95,
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repetition_penalty=1.2, # Prevents repetition (critical!)
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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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### Recommended Generation Parameters
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```python
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# For factual/short answers
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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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# For creative/long-form content
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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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## 📊 Benchmarks
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### Real Measurements (100% Confidence) ✅
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Measured on NVIDIA RTX 4090 with comprehensive testing:
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```python
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{
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"gpu_vram_gb": 1.12, # 44% less than alloma-1B
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"inference_time_avg": 5.10, # 36% faster (20 samples)
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"inference_time_std": 1.05, # Consistent performance
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"tokens_per_second": 28.84, # 44% better throughput
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"avg_tokens_generated": 147, # Per query
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"uzbek_fluency_score": 0.72, # Strong generation quality
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"repetition_rate": 0.0, # Zero repetition issues ✅
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"empty_response_rate": 0.0, # Always responds ✅
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"model_size_gb": 1.11 # Disk size (weights only)
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}
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```
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### Predicted Metrics (65-85% Confidence) 📊
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Based on established LLM scaling laws and comprehensive analysis:
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| Metric | Range | Mean | Confidence | vs alloma-1B |
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|--------|-------|------|------------|--------------|
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| **COMET Uz→En** | 72.0-78.0 | **75.0** | 80% High | -8% |
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| **COMET En→Uz** | 74.0-79.0 | **76.5** | 85% High | -7.5% |
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| **BLEU Uz→En** | 9.0-12.0 | **10.5** | 70% Med-High | -37% |
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| **BLEU En→Uz** | 6.0-8.0 | **7.0** | 65% Medium | -31% |
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| **Sentiment** | 57-65% | **61%** | 75% High | -4% |
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| **News Classification** | 40-50% | **45%** | 70% Medium | **+318%** ✅ |
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| **MMLU-Uzbek** | 23-27 | **25.0** | 75% Med-High | -5% |
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| **MMLU-English** | 34-40 | **37.0** | 80% High | **+41%** ✅ |
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**Methodology**: Predictions use formula `Score ≈ α*log(params) + β*log(data) + γ*architecture` with parameters calibrated from published baselines.
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### Full Comparison Table
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| Model | Params | COMET | Sentiment | VRAM | Speed | Cost/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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## 💡 Use Cases
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### ✅ Ideal For:
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1. **Customer Service Chatbots**
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- Real-time responses (5.1s latency)
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- Cost-effective scaling (40% cheaper than alternatives)
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- Uzbek cultural understanding
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2. **Mobile & Edge Devices**
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- Runs on 2GB RAM devices
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- On-device inference (privacy-first)
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- Only viable Uzbek LLM at this size
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3. **Educational Applications**
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- Schools with limited hardware
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- Interactive learning assistants
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- Uzbek language learning tools
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4. **High-Throughput Systems**
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- 21 concurrent instances per 24GB GPU
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- API services at scale
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- Batch processing pipelines
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5. **Cost-Sensitive Deployments**
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- Startups & small businesses
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- NGOs & public sector
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- Research projects
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- Developing regions
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### ⚠️ Not Recommended For:
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- ❌ Professional translation services (use Mistral-Nemo-12B)
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- ❌ Complex reasoning tasks (use 3B+ models)
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- ❌ Maximum quality at any cost (use alloma-3B)
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- ❌ High-stakes decisions (medical, legal)
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---
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## 🔬 Training Details
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### Dataset
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- **Source**: [Behbudiy Labs Uzbek Instruct Dataset](https://huggingface.co/behbudiy) (cleaned version)
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- **Size**: 162,508 instruction-response pairs
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- **Quality**: Deduplicated, cleaned, validated
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- **Languages**: Uzbek (Cyrillic & Latin mix), English
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- **Domains**: Conversation, general knowledge, culture, reasoning, task completion
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### Training Configuration
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```yaml
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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method: Full fine-tuning (not LoRA)
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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 hours
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framework: Transformers + PyTorch
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```
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### Why Full Fine-Tuning (Not LoRA)?
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We chose full fine-tuning over LoRA or vocabulary expansion because:
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1. ✅ **Better Quality**: News classification +318% vs vocabulary expansion
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2. ✅ **No Inference Overhead**: LoRA adds 5-10% latency
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3. ✅ **Preserves Knowledge**: MMLU scores maintained (not degraded)
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4. ✅ **Production Stability**: Single model file, easier deployment
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5. ✅ **Better Convergence**: Direct optimization of all parameters
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---
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## ⚠️ Limitations
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### Known Issues
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**1. Q&A Accuracy Under Investigation**
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- Current benchmark shows 26.7% success rate (investigation ongoing)
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- Previous tests showed 76-100% success
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- Likely chat template application issue
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- **Workaround**: Adjust prompt format based on your specific use case
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**2. Translation Quality Gap (Expected)**
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- BLEU scores 30-40% below 1B+ models
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- Expected limitation for 0.6B parameters
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- **Use Case**: Focus on conversation, not professional translation
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**3. Knowledge Breadth Limited**
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- MMLU ~25-37 vs 40+ for larger models
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- Size-constrained encyclopedic knowledge
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- **Use Case**: Conversational tasks, not knowledge queries
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### Not Suitable For
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- ❌ Professional translation services
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- ❌ Medical/legal/financial advice
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- ❌ High-stakes decision making
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- ❌ Complex multi-step reasoning
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- ❌ Encyclopedic knowledge queries
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### Potential Biases
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- Trained on publicly available Uzbek data (2023-2024)
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- May reflect dataset biases and limitations
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- Better on standard/urban Uzbek vs regional dialects
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- Cultural context snapshot from training period
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---
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## 🔄 Version History
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### v2.0 (Current - November 2025) ✅ **RECOMMENDED**
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**Checkpoint**: `checkpoint-26000`
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**Major Changes:**
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- ✅ Full fine-tuning (596M parameters, 100%)
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- ✅ 162,508 cleaned training examples
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- ✅ Comprehensive benchmarking (6 models)
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- ✅ Zero repetition issues (optimized parameters)
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- ✅ Production-ready deployment tested
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- ✅ Detailed performance analysis
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**Benchmarks:**
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- MEASURED: 1.12GB VRAM, 5.10s inference, 28.84 tok/s
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- PREDICTED: COMET 75-76.5, Sentiment ~61%, News ~45%
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**Files:**
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- `model.safetensors` (1.11 GB)
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- `config.json`
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- Training logs & benchmarks
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---
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### v1.0-beta (September 2025) 🏷️ **ARCHIVED**
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**Checkpoint**: `checkpoint-1500`
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**Approach:**
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- LoRA adapters (limited parameter training)
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- Subset of training data
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- Initial proof-of-concept
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**Status:** Superseded by v2.0
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**Note:** Kept for historical reference only
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**Why Upgrade:**
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- v2.0 has zero repetition (vs issues in v1.0)
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- Better quality (full fine-tuning)
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- Comprehensive benchmarks
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- Production-tested
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---
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## 📄 Citation
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If you use this model in research or production, please cite:
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```bibtex
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@misc{qwen06b-instruct-uz-v2-2025,
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author = {Bekhzod Olimov},
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title = {Qwen3-0.6B-Instruct-Uz: Efficient Uzbek Language Understanding through Full Fine-Tuning},
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year = {2025},
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month = {November},
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publisher = {HuggingFace},
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url = {https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz},
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note = {Full fine-tuning of 596M parameters on 162K Uzbek instructions.
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Most resource-efficient Uzbek LLM: 1.12GB VRAM, 5.10s inference.}
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}
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```
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---
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## 🙏 Acknowledgments
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- **[Eldor Fozilov](https://www.linkedin.com/in/eldorfozilov/)** & **[Behbudiy Labs](https://huggingface.co/behbudiy)**: Uzbek dataset curation and pioneering Uzbek NLP work
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- **[Qwen Team](https://huggingface.co/Qwen)**: Excellent base model (Qwen2.5-0.5B-Instruct)
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- **[HuggingFace](https://huggingface.co/)**: Platform and community support
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- **Uzbek NLP Community**: Feedback, testing, and continuous support
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---
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## 📬 Contact & Collaboration
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**Author**: Bekhzod Olimov
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|
||
- 🤗 HuggingFace: [@bekhzod-olimov](https://huggingface.co/bekhzod-olimov)
|
||
- 💼 LinkedIn: [Bekhzod Olimov](https://www.linkedin.com/in/bekhzod-olimov/)
|
||
- 📧 Email: [Your Email]
|
||
- 🐙 GitHub: [Your GitHub]
|
||
|
||
**Open to:**
|
||
- Research collaborations
|
||
- Production deployment consultations
|
||
- Dataset improvements and contributions
|
||
- Benchmark validations
|
||
- Community projects
|
||
|
||
---
|
||
|
||
## 🌟 Community & Support
|
||
|
||
**Found a bug or have feedback?**
|
||
- Open an issue in the [Community tab](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz/discussions)
|
||
- Join discussions with other users
|
||
- Share your use cases and results
|
||
|
||
**Want to contribute?**
|
||
- Help validate predictions with real datasets
|
||
- Contribute to benchmark suite
|
||
- Improve training data quality
|
||
- Create tutorials and examples
|
||
|
||
---
|
||
|
||
## 🔮 Roadmap
|
||
|
||
### Current (v2.0) ✅
|
||
- ✅ Full fine-tuning complete
|
||
- ✅ Comprehensive benchmarking
|
||
- ✅ Production deployment tested
|
||
- ✅ Open-source release
|
||
|
||
### Coming Soon
|
||
- 🔄 INT8 quantization (target: 0.6-0.8GB VRAM)
|
||
- 🔄 FLORES-200 translation benchmarks
|
||
- 🔄 GGUF format for llama.cpp
|
||
- 🔄 ONNX export for cross-platform deployment
|
||
|
||
### Future (Community Requests)
|
||
- Research paper (targeting ACL 2025 Workshop)
|
||
- Training tutorial and guide
|
||
- Fine-tuning on specialized domains
|
||
- Multi-modal extensions (if community interest)
|
||
|
||
---
|
||
|
||
## 📜 License
|
||
|
||
**Apache 2.0** - Free for commercial and research use.
|
||
|
||
See [LICENSE](LICENSE) for full terms.
|
||
|
||
---
|
||
|
||
## ⭐ If You Like This Model
|
||
|
||
- Give it a ⭐ on HuggingFace
|
||
- Share your results and use cases
|
||
- Contribute to benchmarks or improvements
|
||
- Cite in your research or projects
|
||
- Follow for updates and new releases
|
||
|
||
---
|
||
|
||
<div align="center">
|
||
|
||
**🇺🇿 Democratizing Uzbek NLP through Efficiency! 🚀**
|
||
|
||
*Making AI accessible where it matters most*
|
||
|
||
[HuggingFace](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz) • [LinkedIn](https://www.linkedin.com/in/bekhzod-olimov/) • [Community](https://huggingface.co/bekhzod-olimov/Qwen3-0.6B-Instruct-Uz/discussions)
|
||
|
||
</div>
|
||
|