Instructions to use MOKSHm/t5-base-grammar-synthesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MOKSHm/t5-base-grammar-synthesis with Transformers:

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

pipe = pipeline("text-generation", model="MOKSHm/t5-base-grammar-synthesis")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("MOKSHm/t5-base-grammar-synthesis")
model = AutoModelForSeq2SeqLM.from_pretrained("MOKSHm/t5-base-grammar-synthesis", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MOKSHm/t5-base-grammar-synthesis with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MOKSHm/t5-base-grammar-synthesis"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MOKSHm/t5-base-grammar-synthesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MOKSHm/t5-base-grammar-synthesis

SGLang

How to use MOKSHm/t5-base-grammar-synthesis 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 "MOKSHm/t5-base-grammar-synthesis" \
    --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": "MOKSHm/t5-base-grammar-synthesis",
		"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 "MOKSHm/t5-base-grammar-synthesis" \
        --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": "MOKSHm/t5-base-grammar-synthesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MOKSHm/t5-base-grammar-synthesis with Docker Model Runner:
```
docker model run hf.co/MOKSHm/t5-base-grammar-synthesis
```

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

Model

This model utilizes the Flan-T5-base pre-trained model and has been fine-tuned using the JFLEG dataset with the assistance of the Happy Transformer framework. Its primary objective is to correct a wide range of potential grammatical errors that sentences might contain including issues with punctuation, typos, prepositions, and more.

Usage with Transformers

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Sajid030/t5-base-grammar-synthesis")
model = AutoModelForSeq2SeqLM.from_pretrained("Sajid030/t5-base-grammar-synthesis")

text = "One person if do n't have good health that means so many things they could lost ."
inputs = tokenizer("grammar:"+text, truncation=True, return_tensors='pt')

output = model.generate(inputs['input_ids'])
correction=tokenizer.batch_decode(output, skip_special_tokens=True)
print("".join(correction)) #Correction: If one person doesn't have good health, so many things could be lost.

Usage with HappyTransformers

from happytransformer import HappyTextToText, TTSettings
happy_tt = HappyTextToText("T5", "Sajid030/t5-base-grammar-synthesis")
args = TTSettings()

sentence = "Much many brands and sellers still in the market."
result = happy_tt.generate_text("grammar: "+ sentence, args=args)

print(result.text) # Many brands and sellers are still in the market.

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Safetensors

Model size

0.2B params

Tensor type

F32

MOKSHm
/

t5-base-grammar-synthesis

Model

Usage with Transformers

Usage with HappyTransformers

Dataset used to train MOKSHm/t5-base-grammar-synthesis