Instructions to use stukenov/sozkz-core-llama-600m-kk-gec-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stukenov/sozkz-core-llama-600m-kk-gec-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="stukenov/sozkz-core-llama-600m-kk-gec-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("stukenov/sozkz-core-llama-600m-kk-gec-v1")
model = AutoModelForMultimodalLM.from_pretrained("stukenov/sozkz-core-llama-600m-kk-gec-v1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use stukenov/sozkz-core-llama-600m-kk-gec-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stukenov/sozkz-core-llama-600m-kk-gec-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stukenov/sozkz-core-llama-600m-kk-gec-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/stukenov/sozkz-core-llama-600m-kk-gec-v1

SGLang

How to use stukenov/sozkz-core-llama-600m-kk-gec-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "stukenov/sozkz-core-llama-600m-kk-gec-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stukenov/sozkz-core-llama-600m-kk-gec-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "stukenov/sozkz-core-llama-600m-kk-gec-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stukenov/sozkz-core-llama-600m-kk-gec-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use stukenov/sozkz-core-llama-600m-kk-gec-v1 with Docker Model Runner:
```
docker model run hf.co/stukenov/sozkz-core-llama-600m-kk-gec-v1
```

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SozKZ Core Llama 600M — Kazakh GEC v1

Казақ тіліне арналған грамматикалық қателерді түзету моделі (Grammar Error Correction). 600M параметр.

Model Details

Parameter	Value
Architecture	LlamaForCausalLM (decoder-only)
Parameters	587M
Base model	`stukenov/sozkz-core-llama-600m-kk-base-v1`
Training data	sozkz-corpus-synthetic-kk-gec-v1
Training	3 epochs, LR=1.5e-5, BS=128, cosine schedule
Clean ratio	80%
Data filter	word edit distance ≤ 2
Tag	Single `<грамматика>` (unified)
Format	Thinking (💭 diff → corrected)
Hardware	2× RTX 4090 (vast.ai), ~1.7h
License	MIT (gated access)

Format

<грамматика> ошибочный текст
💭 слово1→слово2 (описание)
→ исправленный текст

Usage

from transformers import AutoModelForCausalLM, GPT2TokenizerFast
from huggingface_hub import hf_hub_download
import torch

model_id = "stukenov/sozkz-core-llama-600m-kk-gec-v1"
tok_file = hf_hub_download(repo_id=model_id, filename="tokenizer.json")
tokenizer = GPT2TokenizerFast(tokenizer_file=tok_file)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
model.eval()

def correct(text):
    prompt = f"<грамматика> {text}\n"
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=480)
    with torch.no_grad():
        out = model.generate(**inputs, max_new_tokens=len(inputs["input_ids"][0])+60,
            temperature=0.3, top_p=0.9, do_sample=True, repetition_penalty=1.1,
            pad_token_id=tokenizer.eos_token_id)
    result = tokenizer.decode(out[0], skip_special_tokens=True)
    if "→ " in result: result = result.split("→ ", 1)[1]
    for stop in ["\n<", "\n\n"]:
        if stop in result: result = result[:result.index(stop)]
    return result.strip() or text

print(correct("Студенттар университетте оқиды."))

Part of SozKZ Project

This model is part of the SozKZ project for Kazakh language AI.

Benchmark Results

Evaluated on 100-example custom GEC test (pure model inference, no pre/post pipeline).

🏆 Лучшая модель в SozKZ GEC benchmark (100-example custom test)

Category	Score
Орфография (емле)	15/30 (50%)
Грамматика	12/20 (60%)
Пунктуация	3/15 (20%)
Смешанный	2/20 (10%)
Identity preservation	15/15 (100%)
Total	47/100 (47%)

Leaderboard (100-example custom benchmark)

Модель	Total	Емле/30	Грамм/20	Пункт/15	Смеш/20	Ident/15
sozkz-core-llama-600m-kk-gec-v1	47%	15	12	3	2	15/15
sozkz-fix-qwen-500m-kk-gec-v3	38%	0	16	9	0	13/15
sozkz-core-llama-300m-kk-gec-v4	37%	9	6	4	3	15/15
sozkz-fix-qwen-500m-kk-gec-v1	35%	0	12	8	0	15/15
sozkz-fix-qwen-500m-kk-gec-v2	30%	0	11	7	0	12/15
sozkz-core-llama-1b-kk-gec-v1	16%	2	6	1	0	7/15
sozkz-fix-qwen-500m-kk-gec-v4	5%	0	1	4	0	0/15
sozkz-fix-mt5b-kk-gec-run13-v1	5%	0	2	0	0	3/15
sozkz-nllb-1b-kk-gec-v1	1%	0	1	0	0	0/15
sozkz-nllb-1b-kk-pretrain-v1	1%	0	1	0	0	0/15
sozkz-core-llama-300m-kk-gec-v3	1%	0	1	0	0	0/15
sozkz-core-llama-300m-kk-gec-v1/v2a/v2b	0–1%	0	0	0	0	0–1
sozkz-fix-mt5-50m-kk-gec-v1	0%	0	0	0	0	0/15

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Safetensors

Model size

0.6B params

Tensor type

BF16

Collection including stukenov/sozkz-core-llama-600m-kk-gec-v1

SozKZ GEC: Kazakh Grammar Error Correction

Collection

Grammar error correction models and datasets for Kazakh — Llama GEC (300M, 600M), mT5 GEC, morphology models • 10 items • Updated Apr 7