---
license: apache-2.0
language:
  - uz
  - en
  - ru
pipeline_tag: text-generation
tags:
  - uzbek
  - o'zbek
  - chat
  - instruction-tuned
library_name: transformers
---

# MustaqiLLM

MustaqiLLM is a 5.17-billion-parameter Uzbek **chat and text-classification model**. It
follows Uzbek instructions reliably, writes fluent Uzbek in both Latin and Cyrillic
script, and is strong on sentiment and news classification. It is **not** a knowledge
model: on multiple-choice knowledge benchmarks it performs at chance. Read the
[Evaluation](#evaluation) and [Limitations](#limitations) sections before using it —
they are specific about what works and what does not.

| | |
|---|---|
| Parameters | 5.17 B |
| Architecture | `NeuronLMForCausalLM` (custom, ships with the repo) |
| Layers / hidden | 36 / 3584 |
| Attention | GQA, 28 query heads : 4 KV heads, head_dim 128, QK-norm |
| Position encoding | RoPE, θ = 500000 |
| Context length | 4096 tokens |
| Vocabulary | 48,000 (BPE) |
| Embeddings | untied |
| Weights dtype | bfloat16 (embeddings and `lm_head` stored fp32) |
| Languages | Uzbek (Latin + Cyrillic), English, Russian |

---

## Quick start

The architecture is custom, so `trust_remote_code=True` is **required** — the modeling
code ships inside this repository.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "NeuronUz/MustaqiLLM"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,       # weights are bf16; do not load in fp32
    device_map="cuda",
).eval()

messages = [{"role": "user", "content": "O'zbekistonning poytaxti qaysi shahar?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=256,
        do_sample=False,           # greedy is fine for a short answer like this;
                                   # for open chat use the sampling settings below
        eos_token_id=5,            # <|im_end|> -- also the repo default
        pad_token_id=3,            # <pad>
    )

print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

```
Oʻzbekistonning poytaxti - Toshkent.
```

### Chat template

The model uses ChatML. `tokenizer.apply_chat_template` applies it for you; the raw form is:

```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{assistant}<|im_end|>
```

A system turn is optional, and for general chat you should leave it out — a generic
system prompt measurably increases repetition (see Generation settings). Task-specific
system prompts, in Uzbek, work well.

### Generation settings

These are measured, not guessed. 35 decoding configurations were swept over 120 held-out
Uzbek prompts across 14 categories with 2 seeds each — 8,400 generations — scored
automatically for verbatim sentence repetition and for failure to emit `<|im_end|>`
within the token budget. Because those metrics see repetition but not fluency, the
finalists were then compared head-to-head by an LLM judge over 1,200 pairwise
judgements with randomised A/B order.

**Recommended for open chat:**

```python
out = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.05,   # not optional -- see below (1.05-1.10 all work)
    use_cache=True,
)
```

| setting | value | why |
|---|---|---|
| `repetition_penalty` | **1.05–1.10** for chat | The single most important setting. Without it the model restates whole sentences verbatim. Duplicate-sentence rate at temperature 0.7: **4.0% at `1.00`, 1.5% at `1.03`, 1.3% at `1.05`, 1.0% at `1.10`, 0.1% at `1.15`.** Do not read that as "higher is better" — see the note below the table. |
| `do_sample` / `temperature` / `top_p` | `True`, `0.7`, `0.9` for chat; `False` (greedy) for classification, extraction and short answers | `generation_config.json` ships `do_sample: true` with **no** `temperature` or `top_p`, so the unconfigured default is temperature 1.0 / top_p 1.0 — pass these explicitly. Terse tasks showed a 0% repetition rate under every configuration tested, so greedy is safe there. |
| `eos_token_id` | **5** (`<\|im_end\|>`) | The turn terminator, already the default in `config.json` / `generation_config.json` — you do not need to pass it. Do **not** override it with the pretraining EOS (`</s>`), which never appears in chat data: generation would then run to `max_new_tokens`. |
| system prompt | **omit it** for general chat | A generic system turn measurably degrades output. Duplicate-sentence rate over a 24-prompt subset: **0.0% with no system prompt, 1.9% with a generic Uzbek one, 5.2% with a generic English one** (at temperature 0.7, `repetition_penalty` 1.05); without a repetition penalty the same comparison is 11.2% / 23.7% / 14.9%. Task-specific system prompts (a required format, a persona) are fine — it is the generic "you are a helpful assistant" turn that hurts. |
| `dtype` | `torch.bfloat16` | Trained in bf16. `float16` is also safe — no overflow, and output quality is indistinguishable — so pre-Ampere GPUs are supported. `float32` doubles memory for **half** the throughput (205 vs 412 tok/s) and changes nothing. |

**More penalty is not better past ~1.10.** The automatic metrics keep improving as
`repetition_penalty` rises, but fluency does not. Judged head-to-head on the same
prompts, `rp=1.15` — the cleanest configuration by repetition metrics — *lost* to gentler
settings: 30.6% win rate against `rp=1.10` and 38.8% against `rp=1.05`. Between 1.05 and
1.10 the judge is a coin flip (52.2%), so anywhere in that band is fine. Below it there
is a real floor: `rp=1.05` beats `rp=1.03` at 60.4%. Sampling with a penalty beats greedy
outright (58.8%).

**Greedy decoding degrades as the output gets longer**, which is why it is recommended
above only for short outputs. Over the full 120-prompt sweep at a 384-token budget,
greedy produced 17.1% duplicate sentences and failed to terminate on 21.7% of prompts,
against 1.3% and 4.2% for `t=0.7, rp=1.05`. On chat and long-form prompts with a
768-token budget the gap widens:

| configuration | never emits `<\|im_end\|>` | duplicate sentences | worst case |
|---|---:|---:|---:|
| greedy | 23.1% | 27.3% | one sentence repeated **9.8×** |
| `t=0.7, top_p=0.9` | 15.4% | 8.0% | 2.2× |
| `t=0.7, top_p=0.9, rp=1.05` | 11.5% | 3.0% | 1.7× |
| **`t=0.7, top_p=0.9, rp=1.10`** | **0.0%** | **0.8%** | **1.1×** |

Lowering the temperature makes this worse, not better, because sharpening the
distribution locks the model into the repeat loop. Without a repetition penalty,
duplicate sentences rise from 1.5% at temperature 0.9 to 8.8% at 0.5; a separate probe
at temperature 0.3 reached 19.1%, the worst of any configuration tested. Determinism is genuinely in tension with quality
here: greedy plus `repetition_penalty=1.10` still leaves 7.9% duplicate sentences —
better than greedy alone, but far short of sampling. If you need reproducible output,
sample with a fixed seed rather than decoding greedily.

Two categories are much harder than the rest and need a larger `max_new_tokens`: Uzbek
**Cyrillic** prompts (37.6% hit the token cap, 12.4% duplicate sentences, pooled across
all configurations) and **refusals** (21.8% and 9.2%) — the model has trouble ending a
turn once it starts declining a request. Everything else — translation, short answers,
grammar and style rewriting, multi-turn — sat at or near 0% on both metrics under every
configuration tested.

Batch size changes greedy output: identical prompts decoded at batch 1 and batch 12
matched in only 24 of 32 cases, because left-padding shifts the numerics. Fix the batch
size when comparing runs.

Memory: the checkpoint is 11.0 GB on disk (embeddings and `lm_head` are stored fp32); loading with
`dtype=torch.bfloat16` as above casts them down to ~10.3 GB of weights, so a single 16 GB GPU is
enough for inference.

`config.json` sets `use_cache: false`, but `generation_config.json` sets `use_cache: true`, so
`generate()` uses the KV cache. Pass `use_cache=True` explicitly if you write your own decode loop.

### Classification

The model is usable as a constrained label picker: put the label set in the prompt, ask
for the label only, decode **greedily**, and cap `max_new_tokens`. Terse tasks showed a
0% repetition rate under every decoding configuration tested, so no repetition penalty
is needed here — and greedy keeps the output reproducible.

These are the exact prompts behind the news (0.6531) and sentiment (0.9259) scores in
[Evaluation](#uzbek-benchmarks). Reuse them verbatim to reproduce those numbers.

```python
import re
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "NeuronUz/MustaqiLLM"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    device_map="cuda",
).eval()


def classify(prompt: str, text: str, max_chars: int = 4000) -> str:
    if len(text) > max_chars:
        text = text[:max_chars].rsplit(" ", 1)[0]
    inputs = tokenizer.apply_chat_template(
        [{"role": "user", "content": prompt.format(text=text)}],
        add_generation_prompt=True,
        return_tensors="pt",
        return_dict=True,
    ).to(model.device)

    with torch.no_grad():
        out = model.generate(
            **inputs,
            max_new_tokens=12,     # a label is a few tokens; do not give it room to ramble
            do_sample=False,       # greedy -- labels must be deterministic
            pad_token_id=3,        # <pad>
        )
    return tokenizer.decode(
        out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True
    ).strip()
```

**News topic, 10-way.** Numbered labels: one digit is easier to emit and to parse than a
multi-word category name.

```python
NEWS_LABELS = [
    "Siyosat", "Iqtisodiyot", "Texnologiya", "Sport", "Madaniyat",
    "Salomatlik", "Oila va Jamiyat", "Ta'lim", "Ekologiya", "Xorijiy Yangiliklar",
]

NEWS_PROMPT = (
    "Classify the given Uzbek news article into one of the following categories. "
    "Respond with only the category number.\n\n"
    + "".join(f"{i} - {name}\n" for i, name in enumerate(NEWS_LABELS))
    + "\nArticle: {text}\n\nAnswer:"
)

raw = classify(NEWS_PROMPT, "O'zbekiston Markaziy banki asosiy stavkani o'zgarishsiz qoldirdi.")
match = re.search(r"\d+", raw)
label = NEWS_LABELS[int(match.group())] if match and int(match.group()) < 10 else None
print(raw, "->", label)
```

```
1 -> Iqtisodiyot
```

**Sentiment, binary.**

```python
SENTIMENT_PROMPT = (
    "Given the following Uzbek text, determine the sentiment as either "
    "'Positive' or 'Negative'. Respond with only one label.\n\n"
    "Text: {text}\n\nLabel:"
)

raw = classify(SENTIMENT_PROMPT, "Mahsulot juda sifatli, yetkazib berish tez bo'ldi.")
print(raw)   # Positive
```

**Your own label set.** The same shape works for any closed label set — put one label
per line, demand the label (or its number) and nothing else, and parse the output with a
prefix match or a regex rather than an exact-string comparison, so a stray token never
becomes an invalid prediction. Two practical notes:

- **A task-specific system prompt is fine here** and often helps — it is the generic
  "you are a helpful assistant" turn that degrades output (see
  [Generation settings](#generation-settings)). Put the required output format in it.
- **English prompt text with Uzbek labels** is what was measured. Uzbek prompt wording
  also works; if you change the wording, re-measure — label boundaries (especially
  `Siyosat` vs `Xorijiy Yangiliklar`, and `Oila va Jamiyat`, the weakest class at 0.4273)
  are sensitive to how the categories are described.
- **Do not batch-compare greedy runs at different batch sizes.** Left-padding shifts the
  numerics; identical prompts matched in only 24 of 32 cases between batch 1 and batch 12.

### Serving

**vLLM and SGLang cannot load this model.** They reimplement each architecture
internally rather than executing a repository's Python, and `NeuronLMForCausalLM` is not
in their model registries — `trust_remote_code` only covers the config and tokenizer
there. Use the `transformers` backend, or convert the weights (the architecture is
Qwen3-equivalent apart from *fused* `qkv_proj` / `gate_up_proj` and `out_proj` naming;
splitting those tensors and renaming to the Qwen3 layout yields a checkpoint vLLM will
serve).

---

## Evaluation

Full public benchmark suite, greedy decoding, `transformers` backend, seed 42, complete
test sets (no subsampling). Scores are accuracy unless noted.

### Uzbek benchmarks

| benchmark | n | score | invalid rate |
|---|---:|---:|---:|
| uzlib (Uzbek linguistic MCQ) | 1,861 | 0.2875 | 0.0000 |
| TUMLU-Uzbek (Uzbek MMLU) | 700 | 0.3286 | 0.0000 |
| MMLU-Uz (translated MMLU) | 14,042 | 0.2584 | 0.0000 |
| News topic classification (10-way, `risqaliyevds/uzbek-zero-shot-classification`) | 96,970 | **0.6531** | 0.0000 |
| Sentiment (binary) | 10,000 | **0.9259** | 0.0001 |

Random baselines: 0.25 for the 4-way MCQ tasks, 0.10 for news, 0.50 for sentiment.

### English

| benchmark | n | score | invalid rate |
|---|---:|---:|---:|
| MMLU (English) | 14,042 | 0.2619 | 0.0000 |

### Translation (FLORES+)

| direction | n | BLEU | COMET | length ratio |
|---|---:|---:|---:|---:|
| English → Uzbek | 2,009 | 5.17 | 0.7397 | 1.018 |
| Uzbek → English | 2,009 | 1.83 | 0.5376 | 1.229 |

### uzlib, per split

| split | n | score |
|---|---:|---:|
| fill_in | 52 | 0.3077 |
| correct_word (orthography) | 1,501 | 0.3011 |
| meaning_in_context | 72 | 0.2639 |
| meaning | 236 | 0.2034 |

### News, per class

| class | n | score |
|---|---:|---:|
| Sport | 16,113 | 0.8743 |
| Texnologiya (Technology) | 5,177 | 0.7309 |
| Madaniyat (Culture) | 2,405 | 0.7081 |
| Siyosat (Politics) | 29,500 | 0.6794 |
| Iqtisodiyot (Economy) | 10,755 | 0.6596 |
| Salomatlik (Health) | 3,505 | 0.6579 |
| Ta'lim (Education) | 1,987 | 0.6548 |
| Ekologiya (Ecology) | 1,784 | 0.5667 |
| Xorijiy Yangiliklar (World news) | 11,732 | 0.5124 |
| Oila va Jamiyat (Family & Society) | 14,012 | 0.4273 |

## Limitations

- **MCQ knowledge tasks are at chance.** uzlib, MMLU-Uz and MMLU-English all sit within noise of the 0.25 baseline over ~30,000 questions, with near-zero invalid rates — correct format, wrong answer. This is missing knowledge, not parsing. Do not use it for factual QA, exams, or retrieval-free knowledge tasks. TUMLU-Uzbek (0.3286) is the only MCQ result above chance, on a 700-item sample (±3.5%).
- **Uzbek → English translation is weak** (BLEU 1.83, length ratio 1.229): it over-generates. English → Uzbek is usable (COMET 0.7397) but below dedicated MT systems.
- **Script conversion does not work** despite being trained for it — Latin→Cyrillic requests often return the input unchanged.
- **Cyrillic artifacts.** The Cyrillic data was machine-transliterated; loanwords and brand names can be mangled (`Facebook` → `Факебоок`) and stray Cyrillic characters leak into Latin words. Cyrillic chat is coherent, but its orthography is less reliable than Latin.
- **Self-identification.** Identity data predates the current name, so the model calls itself "NeuronAI 5B".
- **Uneven news classification:** 0.4273 on the diffuse "Oila va Jamiyat" class vs 0.8743 on Sport.
- **Safety.** No safety alignment, RLHF, or red-teaming; no refusal training beyond what the instruction data incidentally contains. It can produce incorrect, biased, or unsafe content and will state false facts fluently. Evaluate before any user-facing deployment.

---

## Intended use

**Suitable for:** Uzbek-language chat and assistance; text classification (sentiment,
topic); Uzbek text generation and rewriting in Latin or Cyrillic; English → Uzbek
translation where approximate meaning suffices; a base for further fine-tuning.

**Not suitable for:** factual question answering or anything knowledge-intensive;
exam-style multiple choice; Uzbek → English translation; script transliteration; any
application where a confidently-stated wrong fact causes harm (medical, legal, financial
advice).

## License

Apache 2.0. Training data licensing follows the sources of the underlying public
datasets.

## Citation

```bibtex
@misc{mustaqillm,
  title  = {MustaqiLLM: an instruction-tuned Uzbek language model},
  author = {NeuronUz},
  year   = {2026},
  url    = {https://huggingface.co/NeuronUz/MustaqiLLM}
}
```