Instructions to use Harshathemonster/t5-small-updated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Harshathemonster/t5-small-updated with Transformers:

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

pipe = pipeline("text-generation", model="Harshathemonster/t5-small-updated")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Harshathemonster/t5-small-updated")
model = AutoModelForSeq2SeqLM.from_pretrained("Harshathemonster/t5-small-updated")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Harshathemonster/t5-small-updated with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Harshathemonster/t5-small-updated

SGLang

How to use Harshathemonster/t5-small-updated 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 "Harshathemonster/t5-small-updated" \
    --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": "Harshathemonster/t5-small-updated",
		"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 "Harshathemonster/t5-small-updated" \
        --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": "Harshathemonster/t5-small-updated",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Harshathemonster/t5-small-updated with Docker Model Runner:
```
docker model run hf.co/Harshathemonster/t5-small-updated
```

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

T5-Small Grammar Correction

A fine-tuned t5-small model for correcting grammar errors in English text. Given a sentence, the model generates a grammatically correct version using a text-to-text approach.

Model Details

Developed by: Harsha Vardhan N
Model type: Sequence-to-Sequence Transformer
Language(s): English
License: Apache 2.0
Finetuned from model: t5-small

Training Details

Training Data

The model was fine-tuned on the wiki_auto/auto_full_with_split dataset, a large-scale corpus designed for sentence-level grammatical and stylistic simplification. It contains aligned pairs of complex and simplified English sentences extracted from Wikipedia and Simple Wikipedia. For this task, the dataset was used to teach the model how to correct ungrammatical sentences into fluent and grammatically correct English.

Training Procedure

Epochs: 3
Training Duration: ~1 hour
Optimizer: AdamW (via Hugging Face Seq2SeqTrainer)
Learning Rate: 5e-5
Batch Size: 8
Environment: Google Colab GPU

Technical Specifications

Compute Infrastructure

Hardware

GPU: Google Colab-provided GPU (likely Tesla T4)

Software

Framework: Hugging Face Transformers, PyTorch
Trainer Used: Seq2SeqTrainer

Downloads last month: 7

Safetensors

Model size

60.5M params

Tensor type

F32

Model tree for Harshathemonster/t5-small-updated

Base model

google-t5/t5-small

Finetuned

(2284)

this model

Harshathemonster
/

t5-small-updated