Instructions to use alexue4/text-normalization-ru-new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alexue4/text-normalization-ru-new with Transformers:

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

pipe = pipeline("text-generation", model="alexue4/text-normalization-ru-new")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("alexue4/text-normalization-ru-new")
model = AutoModelForSeq2SeqLM.from_pretrained("alexue4/text-normalization-ru-new", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alexue4/text-normalization-ru-new with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alexue4/text-normalization-ru-new"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alexue4/text-normalization-ru-new",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alexue4/text-normalization-ru-new

SGLang

How to use alexue4/text-normalization-ru-new 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 "alexue4/text-normalization-ru-new" \
    --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": "alexue4/text-normalization-ru-new",
		"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 "alexue4/text-normalization-ru-new" \
        --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": "alexue4/text-normalization-ru-new",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alexue4/text-normalization-ru-new with Docker Model Runner:
```
docker model run hf.co/alexue4/text-normalization-ru-new
```

text-normalization-ru-new / README.md

alexue4

Update README.md

5d0453c verified about 2 years ago

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metadata

license: mit
base_model: cointegrated/rut5-small
tags:
  - generated_from_trainer
model-index:
  - name: text-normalization-ru-new
    results: []
language:
  - ru
pipeline_tag: text2text-generation

text-normalization-ru-new

This model is a fine-tuned version of cointegrated/rut5-small on the Google Kadle and Custom dataset. It achieves the following results on the evaluation set:

Loss: 0.0008
Mean Distance: 0
Max Distance: 3

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 150
eval_batch_size: 150
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.1
num_epochs: 15

Framework versions

Transformers 4.37.2
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1