Instructions to use twnlp/ChineseErrorCorrector3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use twnlp/ChineseErrorCorrector3-4B with Transformers:

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

pipe = pipeline("text-generation", model="twnlp/ChineseErrorCorrector3-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("twnlp/ChineseErrorCorrector3-4B")
model = AutoModelForCausalLM.from_pretrained("twnlp/ChineseErrorCorrector3-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use twnlp/ChineseErrorCorrector3-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "twnlp/ChineseErrorCorrector3-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "twnlp/ChineseErrorCorrector3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/twnlp/ChineseErrorCorrector3-4B

SGLang

How to use twnlp/ChineseErrorCorrector3-4B 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 "twnlp/ChineseErrorCorrector3-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "twnlp/ChineseErrorCorrector3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "twnlp/ChineseErrorCorrector3-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "twnlp/ChineseErrorCorrector3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use twnlp/ChineseErrorCorrector3-4B with Docker Model Runner:
```
docker model run hf.co/twnlp/ChineseErrorCorrector3-4B
```

Improve model card: add metadata, paper link and library tags

by nielsr HF Staff - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+56

-28

nielsr

1 day ago

Hi! I'm Niels, part of the community science team at Hugging Face.

I've opened this PR to improve the model card for ChineseErrorCorrector3-4B. Specifically, I've:

Added metadata for library_name, pipeline_tag, language, and base_model.
Linked the model to its associated research paper: CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards.
Included the GitHub repository link for the project.
Organized the evaluation results and sample usage for better readability.

These changes help users discover the model more easily and provide context regarding its research background.

Improve model card: add metadata, paper link and library tagsea6dee36

twnlp changed pull request status to merged about 22 hours ago

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