Instructions to use basic-go/rut5-base-texificator-st1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use basic-go/rut5-base-texificator-st1 with Transformers:

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

pipe = pipeline("text-generation", model="basic-go/rut5-base-texificator-st1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("basic-go/rut5-base-texificator-st1")
model = AutoModelForSeq2SeqLM.from_pretrained("basic-go/rut5-base-texificator-st1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use basic-go/rut5-base-texificator-st1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "basic-go/rut5-base-texificator-st1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "basic-go/rut5-base-texificator-st1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/basic-go/rut5-base-texificator-st1

SGLang

How to use basic-go/rut5-base-texificator-st1 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 "basic-go/rut5-base-texificator-st1" \
    --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": "basic-go/rut5-base-texificator-st1",
		"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 "basic-go/rut5-base-texificator-st1" \
        --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": "basic-go/rut5-base-texificator-st1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use basic-go/rut5-base-texificator-st1 with Docker Model Runner:
```
docker model run hf.co/basic-go/rut5-base-texificator-st1
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

basic-go/rut5-base-texificator-st1

Модель предназначена для нормализации исходного текста, содержащего математические сущности, выраженные в смеси полусловесных формулировок и/или различных математических языков разметки, в текст, приведенный в соответствие с правилами системы компьютерной вёрстки LaTeX для русского языка.

Использование

Пример ниже демонстрирует нормализацию:

from transformers import pipeline


normalizer = pipeline("text2text-generation", model="basic-go/rut5-base-texificator-st1")

inputs = ["неопределённый интеграл жи штрих от икс дэ икс равно жи от икс плюс цэ большое",
    r"f : RR^(2) -> RR^(3)"]
results = normalizer(inputs, max_length=128, do_sample=True, length_penalty=0.5, top_k=100, num_beams=7, early_stopping=True, repetition_penalty=2.5)

print(results)

# [{'generated_text': "\\(\\int g'(x)dx=g(x)+C\\)"}, {'generated_text': '\\(f : \\mathbb{R}^2 \\to \\mathbb{R}^3\\)'}]

Вместе с тем рекомендуется использовать модель в составе библиотеки Emma для актуальной пред- и постобработки данных.

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