Instructions to use CodeTed/Chinese_Grammarly with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeTed/Chinese_Grammarly with Transformers:

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

pipe = pipeline("text-generation", model="CodeTed/Chinese_Grammarly")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("CodeTed/Chinese_Grammarly")
model = AutoModelForSeq2SeqLM.from_pretrained("CodeTed/Chinese_Grammarly")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CodeTed/Chinese_Grammarly with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/CodeTed/Chinese_Grammarly

SGLang

How to use CodeTed/Chinese_Grammarly 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 "CodeTed/Chinese_Grammarly" \
    --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": "CodeTed/Chinese_Grammarly",
		"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 "CodeTed/Chinese_Grammarly" \
        --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": "CodeTed/Chinese_Grammarly",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeTed/Chinese_Grammarly with Docker Model Runner:
```
docker model run hf.co/CodeTed/Chinese_Grammarly
```

Chinese_Grammarly / README.md

CodeTed

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	---
	license: apache-2.0
	datasets:
	- CodeTed/CGEDit_dataset
	language:
	- zh
	metrics:
	- accuracy
	library_name: transformers
	tags:
	- CGED
	- CSC
	pipeline_tag: text2text-generation
	---
	# CGEDit - Chinese Grammatical Error Diagnosis by Task-Specific Instruction Tuning

	Try the model from this space "[Chinese Grammarly](https://huggingface.co/spaces/CodeTed/Chinese-Grammarly)".

	This model was obtained by fine-tuning the corresponding `ClueAI/PromptCLUE-base-v1-5` model on the CoEdIT dataset.
	![CGEDit_model.png](https://cdn-uploads.huggingface.co/production/uploads/64c7473f513a7fa7c32e153b/AtlsZUWz86rKyb_9EWlDa.png)


	## Model Details
	### Model Description
	- Language(s) (NLP): `Chinese`
	- Finetuned from model: `ClueAI/PromptCLUE-base-v1-5`
	### Model Sources
	- Repository: [https://github.com/TedYeh/Chinese_spelling_Correction](https://github.com/TedYeh/Chinese_spelling_Correction)

	## Usage
	```python
	from transformers import AutoTokenizer, T5ForConditionalGeneration

	tokenizer = AutoTokenizer.from_pretrained("CodeTed/Chinese_Grammarly")
	model = T5ForConditionalGeneration.from_pretrained("CodeTed/Chinese_Grammarly")
	input_text = '糾正句子裡的錯字: 看完那段文張，我是反對的！'
	input_ids = tokenizer(input_text, return_tensors="pt").input_ids
	outputs = model.generate(input_ids, max_length=256)
	edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
	```