Qwen3-14B-Base-Uzbek-Cyrillic

A fine-tuned version of Qwen/Qwen3-14B-Base adapted for the Uzbek language in Cyrillic script. The model was trained with LoRA using the Unsloth framework, which improves fluency and grammatical coherence on Uzbek (Cyrillic) text while retaining the multilingual capabilities of the base model.

Model Details

Property	Value
Base model	Qwen/Qwen3-14B-Base
Architecture	Transformer decoder (causal LM)
Parameters	14.8B
Context length	32,768 tokens
Fine-tuning method	LoRA (r=16, alpha=32, dropout=0.0)
Training framework	Unsloth
Precision	bfloat16
Language	Uzbek (Cyrillic), multilingual
License	Apache 2.0

This is a base (non-instruction-tuned) model. It is intended for text completion and continued pretraining workflows rather than turn-based chat out of the box. For conversational use, apply your own chat template or further instruction fine-tuning.

Intended Use

• Text generation and completion in Uzbek (Cyrillic)
• Summarization and content generation in Uzbek
• Multilingual applications targeting Central Asian languages
• A starting point for further task- or instruction-specific fine-tuning

Limitations

• The model is not instruction-tuned and may not follow prompts as a chat model would.
• Output may contain factual errors or biases inherited from the base model and training data.
• Performance on Latin-script Uzbek or other scripts is not the focus of this fine-tune.
• Generated text should be reviewed before use in production or sensitive contexts.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Just-Bax/Qwen3-14B-Base-Uzbek-Cyrillic"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "Ассалому алайкум! Бугунги об-ҳаво ҳақида маълумот:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Pipeline

from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="Just-Bax/Qwen3-14B-Base-Uzbek-Cyrillic",
    torch_dtype="bfloat16",
    device_map="auto",
)
print(pipe("Ўзбекистон пойтахти", max_new_tokens=64)[0]["generated_text"])

vLLM

pip install vllm
vllm serve "Just-Bax/Qwen3-14B-Base-Uzbek-Cyrillic"

curl -X POST "http://localhost:8000/v1/completions" \
  -H "Content-Type: application/json" \
  --data '{
    "model": "Just-Bax/Qwen3-14B-Base-Uzbek-Cyrillic",
    "prompt": "Бир бор экан, бир йўқ экан,",
    "max_tokens": 512,
    "temperature": 0.5
  }'

Unsloth

from unsloth import FastModel

model, tokenizer = FastModel.from_pretrained(
    model_name="Just-Bax/Qwen3-14B-Base-Uzbek-Cyrillic",
    max_seq_length=2048,
)

Training

The model was fine-tuned with LoRA adapters (r=16, alpha=32, dropout=0.0) on Uzbek Cyrillic text using Unsloth in bfloat16 precision. The adapters were merged into the base weights for distribution, so the model can be loaded directly with transformers without additional adapter loading.

License

Released under the Apache 2.0 license, consistent with the base model Qwen/Qwen3-14B-Base.

Citation

If you use this model, please cite the base model and the Unsloth framework:

@misc{qwen3,
  title  = {Qwen3},
  author = {Qwen Team},
  year   = {2025},
  url    = {https://huggingface.co/Qwen/Qwen3-14B-Base}
}