Instructions to use heack/HeackMT5-ZhCleanText1ML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use heack/HeackMT5-ZhCleanText1ML with Transformers:

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

pipe = pipeline("text-generation", model="heack/HeackMT5-ZhCleanText1ML")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("heack/HeackMT5-ZhCleanText1ML")
model = AutoModelForSeq2SeqLM.from_pretrained("heack/HeackMT5-ZhCleanText1ML")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use heack/HeackMT5-ZhCleanText1ML with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "heack/HeackMT5-ZhCleanText1ML"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "heack/HeackMT5-ZhCleanText1ML",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/heack/HeackMT5-ZhCleanText1ML

SGLang

How to use heack/HeackMT5-ZhCleanText1ML 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 "heack/HeackMT5-ZhCleanText1ML" \
    --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": "heack/HeackMT5-ZhCleanText1ML",
		"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 "heack/HeackMT5-ZhCleanText1ML" \
        --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": "heack/HeackMT5-ZhCleanText1ML",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use heack/HeackMT5-ZhCleanText1ML with Docker Model Runner:
```
docker model run hf.co/heack/HeackMT5-ZhCleanText1ML
```

heack commited on Jun 16, 2023

Commit

e1cbf2d

1 Parent(s): cfb349e

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -10

README.md CHANGED Viewed

@@ -1,11 +1,5 @@
 ---
-license: cc-by-nc-sa-4.0
-language:
-- zh
-pipeline_tag: text-cleaning
-tags:
-- mT5
-- text-cleaning
 ---
 # HeackMT5-ZhCleanText1ML: A Text Cleaning Model for Chinese Texts
@@ -20,7 +14,7 @@ This model, `heack/HeackMT5-ZhCleanText1ML`, is a fine-tuned mT5 model for Chine
 |  step    | epoch | learning_rate          | loss   | eval_loss
-|------|-------|------------------------|--------|--------|---------------------|--------------|------------------------|-----------------------|
 | 129000   | 3.73  | 1e-05  | 1.714 | 1.706
@@ -124,9 +118,10 @@ This model is released under the CC BY-NC-SA 4.0 license.
 If you use this model in your research, please cite:
-```bibtex
 @misc{kongyang2023heackmt5ZhCleanText1ML,
     title={heack/HeackMT5-ZhCleanText1ML: A Large-Scale Text Cleaning Model for Chinese Texts},
     author={Kong Yang},
     year={2023}
-}

 ---
+pipeline_tag: text2text-generation
 ---
 # HeackMT5-ZhCleanText1ML: A Text Cleaning Model for Chinese Texts
 |  step    | epoch | learning_rate          | loss   | eval_loss
+|------|-------|------------------------|--------|--------
 | 129000   | 3.73  | 1e-05  | 1.714 | 1.706
 If you use this model in your research, please cite:
+```
 @misc{kongyang2023heackmt5ZhCleanText1ML,
     title={heack/HeackMT5-ZhCleanText1ML: A Large-Scale Text Cleaning Model for Chinese Texts},
     author={Kong Yang},
     year={2023}
+}