Text Generation
Transformers
Safetensors
Hindi
mt5
text2text-generation
grammatical-error-correction
indic-nlp
hindi
gec
Instructions to use manavdhamecha77/GEC-mT5-Small-Hindi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use manavdhamecha77/GEC-mT5-Small-Hindi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="manavdhamecha77/GEC-mT5-Small-Hindi")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("manavdhamecha77/GEC-mT5-Small-Hindi") model = AutoModelForMultimodalLM.from_pretrained("manavdhamecha77/GEC-mT5-Small-Hindi") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use manavdhamecha77/GEC-mT5-Small-Hindi with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "manavdhamecha77/GEC-mT5-Small-Hindi" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "manavdhamecha77/GEC-mT5-Small-Hindi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/manavdhamecha77/GEC-mT5-Small-Hindi
- SGLang
How to use manavdhamecha77/GEC-mT5-Small-Hindi 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 "manavdhamecha77/GEC-mT5-Small-Hindi" \ --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": "manavdhamecha77/GEC-mT5-Small-Hindi", "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 "manavdhamecha77/GEC-mT5-Small-Hindi" \ --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": "manavdhamecha77/GEC-mT5-Small-Hindi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use manavdhamecha77/GEC-mT5-Small-Hindi with Docker Model Runner:
docker model run hf.co/manavdhamecha77/GEC-mT5-Small-Hindi
| { | |
| "add_prefix_space": true, | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "<pad>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "1": { | |
| "content": "</s>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "2": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "additional_special_tokens": [], | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "</s>", | |
| "extra_ids": 0, | |
| "extra_special_tokens": {}, | |
| "legacy": true, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "pad_token": "<pad>", | |
| "sp_model_kwargs": {}, | |
| "tokenizer_class": "MT5Tokenizer", | |
| "unk_token": "<unk>" | |
| } | |