NeuronAI-Uzbek

NeuronAI-Uzbek is a Qwen3-family causal language model fine-tuned to be helpful for Uzbek (primary) and English. This repository contains model weights (safetensors shards), tokenizer files, and a chat template.

Model summary

• Architecture: Qwen3ForCausalLM (decoder-only)
• Dtype: bfloat16
• Layers: 36
• Hidden size: 2560
• Attention heads: 32 (KV heads: 8)
• Vocab size: 180,000
• Max position embeddings: 40,960 (model config)
• Generation defaults
  • temperature=0.6
  • top_p=0.95
  • top_k=20

Note: This model is from the Qwen3 family and is intended to be used with recent transformers.

Training data (token counts)

This model was trained on a mixture of:

• Uzbek: 1.2B tokens
• English: 0.8B tokens

Total: 2.0B tokens.

Training process

We trained NeuronAI-Uzbek in stages:

1. Data preparation

   • Collected Uzbek- and English-language text.
   • Cleaned and normalized text (deduplication/format normalization).
   • Tokenized into a mixed Uzbek/English stream.

2. Model training / adaptation

   • Continued training / adaptation on the mixed corpus (2.0B tokens total) to improve Uzbek capability while retaining English.

3. Supervised fine-tuning (SFT)

4. Export

   • Exported weights to safetensors shards + index.
   • Uploaded to Hugging Face.

Intended use

• Primary: chat assistant for Uzbek, including general Q&A, drafting, summarization, translation (Uzbek↔English), and instruction following.
• Secondary: English chat and general text generation.

Limitations and risks

• The model can generate incorrect or hallucinated information.
• It may reflect biases present in the training data.
• It is not guaranteed safe for medical/legal/financial advice.
• Uzbek language variants/dialects and domain-specific jargon may be weaker.

How to use

Requirements

• transformers (a recent version)
• torch

Text generation (Transformers)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "NeuronUz/NeuronAI-Uzbek"

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

prompt = "Uzbek tilida qisqa va aniq qilib sun'iy intellekt nima ekanligini tushuntir."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=True,
        temperature=0.6,
        top_p=0.95,
        top_k=20,
    )

print(tokenizer.decode(out[0], skip_special_tokens=True))

Chat formatting

This repository includes a chat_template.jinja. Some environments may not automatically load it into the tokenizer; if tokenizer.chat_template is empty, you can set it manually:

from pathlib import Path
from transformers import AutoTokenizer

repo_id = "NeuronUz/NeuronAI-Uzbek"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)

if not getattr(tokenizer, "chat_template", None):
    tokenizer.chat_template = Path("chat_template.jinja").read_text(encoding="utf-8")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Uzbek tilida menga salom ber."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(text)

If you are running in a notebook or environment where the template file is not present locally, download it from the repo first (or copy the template content directly).

Example prompts

• Uzbek:

  • "Quyidagi matnni xulosa qil: ..."
  • "Menga Python'da fayl o'qish misolini ko'rsat."
  • "Inglizchadan o'zbekchaga tarjima qil: ..."

• English:

  • "Explain gradient checkpointing in simple terms."
  • "Summarize this document in bullet points: ..."

License

The license for this release is currently marked as other because the upstream/base and dataset licensing details are not fully specified in this repository. If you want, I can update this section once you confirm the intended license.

Citation

If you use this model, please cite the repository:

@misc{neuronai_uzbek,
  title        = {NeuronAI-Uzbek},
  author       = {NeuronUz},
  howpublished = {\url{https://huggingface.co/NeuronUz/NeuronAI-Uzbek}},
  year         = {2025}
}