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
```

twnlp commited on Aug 4, 2025

Commit

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@@ -9,13 +9,16 @@ license: apache-2.0
 | [twnlp/ChineseErrorCorrector3-4B](https://huggingface.co/twnlp/ChineseErrorCorrector3-4B)   | 语法+拼写 | 使用200万纠错数据进行全量训练，适用于语法纠错和拼写纠错，效果最好，推荐使用。   |
 | [twnlp/ChineseErrorCorrector2-7B](https://huggingface.co/twnlp/ChineseErrorCorrector2-7B)   | 语法+拼写 | 使用200万纠错数据进行多轮迭代训练，适用于语法纠错和拼写纠错，效果较好。 |
 ## 模型评测（NaCGEC Data）
-| Model Name | Prec | Rec | F0.5 |
-|:-----------------|:-----------|:------------|:-------|
-| twnlp/ChineseErrorCorrector3-4B  | 0.743 | 0.7294 | 0.7402 |
-| twnlp/ChineseErrorCorrector2-7B  |0.5686 | 0.57 | 0.5689 |
-| HW_TSC_nlpcc2023_cgec(华为)  | 0.5095 | 0.3129 | 0.4526 |
-| 鱼饼啾啾Plus(北京大学) | 0.5708 | 0.1294 | 0.3394 |
-| CUHK_SU(香港中文大学) | 0.3882 | 0.1558 | 0.2990 |

 | [twnlp/ChineseErrorCorrector3-4B](https://huggingface.co/twnlp/ChineseErrorCorrector3-4B)   | 语法+拼写 | 使用200万纠错数据进行全量训练，适用于语法纠错和拼写纠错，效果最好，推荐使用。   |
 | [twnlp/ChineseErrorCorrector2-7B](https://huggingface.co/twnlp/ChineseErrorCorrector2-7B)   | 语法+拼写 | 使用200万纠错数据进行多轮迭代训练，适用于语法纠错和拼写纠错，效果较好。 |
 ## 模型评测（NaCGEC Data）
+| Model Name        | Model Link                                                                                                              | Base Model                     | Avg        | SIGHAN-2015 | EC-LAW | MCSC   | GPU | QPS     |
+|:------------------|:------------------------------------------------------------------------------------------------------------------------|:-------------------------------|:-----------|:------------|:-------|:-------|:--------|:--------|
+| Kenlm-CSC         | [shibing624/chinese-kenlm-klm](https://huggingface.co/shibing624/chinese-kenlm-klm)                                     | kenlm                          | 0.3409     | 0.3147      | 0.3763 | 0.3317 | CPU     | 9       |
+| Mengzi-T5-CSC     | [shibing624/mengzi-t5-base-chinese-correction](https://huggingface.co/shibing624/mengzi-t5-base-chinese-correction)     | mengzi-t5-base                 | 0.3984     | 0.7758      | 0.3156 | 0.1039 | GPU     | 214     |
+| ERNIE-CSC         | [PaddleNLP/ernie-csc](https://github.com/PaddlePaddle/PaddleNLP/tree/develop/legacy/examples/text_correction/ernie-csc) | PaddlePaddle/ernie-1.0-base-zh | 0.4353     | 0.8383      | 0.3357 | 0.1318 | GPU     | 114     |
+| MacBERT-CSC       | [shibing624/macbert4csc-base-chinese](https://huggingface.co/shibing624/macbert4csc-base-chinese)                       | hfl/chinese-macbert-base       | 0.3993     | 0.8314      | 0.1610 | 0.2055 | GPU     | **224** |
+| ChatGLM3-6B-CSC   | [shibing624/chatglm3-6b-csc-chinese-lora](https://huggingface.co/shibing624/chatglm3-6b-csc-chinese-lora)               | THUDM/chatglm3-6b              | 0.4538     | 0.6572      | 0.4369 | 0.2672 | GPU     | 3       |
+| Qwen2.5-1.5B-CTC  | [shibing624/chinese-text-correction-1.5b](https://huggingface.co/shibing624/chinese-text-correction-1.5b)               | Qwen/Qwen2.5-1.5B-Instruct     | 0.6802     | 0.3032      | 0.7846 | 0.9529 | GPU     | 6       |
+| Qwen2.5-7B-CTC    | [shibing624/chinese-text-correction-7b](https://huggingface.co/shibing624/chinese-text-correction-7b)                   | Qwen/Qwen2.5-7B-Instruct       | 0.8225     | 0.4917      | 0.9798 | 0.9959 | GPU     | 3       |
+| **Qwen3-4B-CTC(Our)** | [twnlp/ChineseErrorCorrector3-4B](https://huggingface.co/twnlp/ChineseErrorCorrector3-4B)                   | Qwen/Qwen3-4B                  | **0.8521** | 0.6340      | 0.9360 | 0.9864 | GPU     | 5       |