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Model: NeuronUz/NeuronAI-Uzbek Source: Original Platform
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README.md
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---
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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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base_model: Qwen/Qwen3-4B
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tags:
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- uzbek
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- qwen3
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- language-model
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- text-generation
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- nlp
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- central-asia
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- low-resource
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- tokenizer-optimization
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datasets:
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- behbudiy/alpaca-cleaned-uz
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- NeuronUz/uzbek-spelling-mcq
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pipeline_tag: text-generation
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model-index:
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- name: NeuronAI-Uzbek
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results:
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- task:
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type: text-generation
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name: Uzbek Language Understanding
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dataset:
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name: UzLiB Benchmark
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type: uzlib
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metrics:
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- type: accuracy
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value: 0.662
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name: Overall Accuracy
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---
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<div align="center">
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# 🇺🇿 NeuronAI-Uzbek
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### The Most Advanced Open-Source Language Model for Uzbek
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[](https://huggingface.co/NeuronUz/NeuronAI-Uzbek)
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[](https://opensource.org/licenses/Apache-2.0)
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[](https://huggingface.co/Qwen/Qwen3-4B)
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**🏆 4th Place Globally | 🥇 1st Place in Uzbekistan on UzLiB Benchmark**
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*Outperforming GPT-4o, Claude 3.5 Sonnet, and Gemini 2.5 Flash on Uzbek language tasks*
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</div>
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---
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## 📊 Key Results
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<div align="center">
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| Achievement | Value |
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|-------------|-------|
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| **UzLiB Overall Score** | **0.662** |
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| **Global Ranking** | **#4** |
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| **Regional Ranking** | **#1 in Uzbekistan** |
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| **Tokenizer Efficiency Improvement** | **+22.5%** vs Qwen3-4B |
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</div>
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---
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## 🏆 UzLiB Benchmark Performance
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NeuronAI-Uzbek achieves exceptional performance on the [UzLiB Benchmark](https://github.com/tahrirchi/uzlib/blob/main/LEADERBOARD.md), the comprehensive evaluation suite for Uzbek language understanding.
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### Leaderboard Position
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[](https://github.com/tahrirchi/uzlib/blob/main/LEADERBOARD.md)
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> **Note**: NeuronAI-Uzbek is the **smallest model** in the top 10, with only **4B parameters**, while competing against models with 100B+ parameters.
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### Performance Comparison vs Original Qwen3-4B
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| Metric | Qwen3-4B (Original) | NeuronAI-Uzbek | Improvement |
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|--------|:-------------------:|:--------------:|:-----------:|
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| **Overall (All)** | 0.345 | **0.662** | **+91.9%** |
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| Correct Word | 0.351 | 0.718 | +104.6% |
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| Meaning | 0.309 | 0.466 | +50.8% |
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| Meaning in Context | 0.347 | 0.333 | -4.0% |
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| Fill-in | 0.327 | 0.385 | +17.7% |
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---
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## 🔤 Tokenizer Efficiency
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We optimized the tokenizer specifically for Uzbek, achieving significantly better tokenization efficiency (lower fertility rate = fewer tokens per word = faster inference and lower costs).
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### Fertility Rate Comparison
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| Model | Fertility Rate | Std Dev | Vocab Size | Improvement vs Qwen3 |
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|-------|:--------------:|:-------:|:----------:|:--------------------:|
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| **NeuronAI-Uzbek (Ours)** 🏆 | **2.67** | 0.15 | 180,000 | **+22.5%** |
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| Gemma 2-9B | 3.15 | 0.22 | 256,000 | +8.3% |
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| LLaMA 3.1-8B | 3.32 | 0.22 | 128,256 | +3.7% |
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| DeepSeek-V3 | 3.32 | 0.21 | 128,815 | +3.4% |
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| Qwen3-4B (Original) | 3.44 | 0.22 | 151,669 | - |
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> **Fertility Rate**: Average number of tokens per word. Lower is better for efficiency.
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<div align="center">
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<img src="assets/fertility_comparison_chart.png" alt="Tokenizer Fertility Rate Comparison" width="700"/>
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</div>
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### What This Means
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- **22.5% fewer tokens** needed to represent Uzbek text
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- **Faster inference** due to shorter sequences
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- **Lower API costs** when deployed
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- **Better context utilization** - fit more content in the same context window
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---
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## 🛠️ Model Details
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### Architecture
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| Property | Value |
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|----------|-------|
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| **Base Model** | Qwen3-4B |
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| **Parameters** | 4 Billion |
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| **Vocabulary Size** | 180,000 tokens |
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| **Context Length** | 32,768 tokens |
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| **Architecture** | Transformer (Decoder-only) |
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| **Precision** | BFloat16 |
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### Training Methodology
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1. **Tokenizer Surgery**: Extended vocabulary with 40,000 Uzbek-optimized tokens
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2. **Embedding Initialization**: Semantic initialization using subword composition
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3. **Continual Pretraining**: Trained on 2B tokens of Uzbek and English text corpus
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4. **Instruction Fine-tuning**: Aligned using Uzbek and English instruction datasets
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### Training Data
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| Dataset | Type | Purpose |
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|---------|------|---------|
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| Uzbek Web Corpus | Pretraining | Language modeling |
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| behbudiy/alpaca-cleaned-uz | SFT | Uzbek instructions |
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| NeuronUz/uzbek-spelling-mcq | SFT | Benchmark-targeted training |
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| vicgalle/alpaca-gpt4 | SFT | English capability retention |
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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
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```
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### Basic Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "NeuronUz/NeuronAI-Uzbek"
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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="auto",
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device_map="auto",
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trust_remote_code=True
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)
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prompt = "O'zbekiston haqida qisqacha ma'lumot bering."
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer(text, 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=512,
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temperature=0.7,
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top_p=0.9,
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do_sample=True
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)
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response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
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print(response)
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```
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### With Thinking Mode (Chain-of-Thought)
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```python
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messages = [
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{"role": "user", "content": "5 ta 3 ga bo'linuvchi 100 dan kichik natural sonlarni toping."}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=True # Enable step-by-step reasoning
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)
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```
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---
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## 📈 Use Cases
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NeuronAI-Uzbek excels at:
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- **📝 Text Generation**: Creative writing, content creation in Uzbek
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- **❓ Question Answering**: Answering questions about Uzbek culture, history, and general knowledge
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- **📚 Reading Comprehension**: Understanding and analyzing Uzbek texts
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- **🔤 Grammar & Spelling**: Uzbek language correctness tasks
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- **🌐 Translation Assistance**: Uzbek-English language tasks
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- **💬 Conversational AI**: Building Uzbek chatbots and assistants
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---
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## ⚠️ Limitations
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- **Knowledge Cutoff**: Training data has a knowledge cutoff date
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- **Hallucinations**: May generate plausible-sounding but incorrect information
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- **Bias**: May reflect biases present in training data
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- **Not for Critical Applications**: Should not be used for medical, legal, or safety-critical applications without human oversight
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---
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## 📜 License
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This model is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0).
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---
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## 🙏 Acknowledgments
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- **Qwen Team** at Alibaba for the excellent Qwen3-4B base model
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- **UzLiB Benchmark** creators for the comprehensive evaluation framework
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- **Uzbek NLP Community** for datasets and linguistic resources
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---
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## 📖 Citation
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```bibtex
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@misc{neuronai-uzbek-2025,
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title={NeuronAI-Uzbek: An Optimized Language Model for Uzbek},
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author={NeuronAI Team},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/NeuronUz/NeuronAI-Uzbek}
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}
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```
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---
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<div align="center">
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**Built with ❤️ in Uzbekistan by [NeuronUz](https://huggingface.co/NeuronUz)**
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</div>
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
|
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": 9728,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
|
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|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:cbea9d8b99b8988b3d26df0e4d1a008f2a25cc7440a34a2002c396e706d3639b
|
||||
size 16589034
|
||||
226888
tokenizer_config.json
Normal file
226888
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:aadf3eb9c169b3eed2bd3562927289add3da75dd921a47bdf8efecbc5f91c449
|
||||
size 5841
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user