Instructions to use ssh2025/brunei-malay-normalizer-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ssh2025/brunei-malay-normalizer-v4 with Transformers:

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

pipe = pipeline("text-generation", model="ssh2025/brunei-malay-normalizer-v4")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ssh2025/brunei-malay-normalizer-v4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ssh2025/brunei-malay-normalizer-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ssh2025/brunei-malay-normalizer-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ssh2025/brunei-malay-normalizer-v4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ssh2025/brunei-malay-normalizer-v4

SGLang

How to use ssh2025/brunei-malay-normalizer-v4 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 "ssh2025/brunei-malay-normalizer-v4" \
    --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": "ssh2025/brunei-malay-normalizer-v4",
		"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 "ssh2025/brunei-malay-normalizer-v4" \
        --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": "ssh2025/brunei-malay-normalizer-v4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ssh2025/brunei-malay-normalizer-v4 with Docker Model Runner:
```
docker model run hf.co/ssh2025/brunei-malay-normalizer-v4
```

brunei-malay-normalizer-v4 / release-authorization.json

ssh2025

Release user-approved V4 frozen Z with documented regression exception

e973108 verified about 2 months ago

Raw History Blame Contribute Delete

417 Bytes

{
  "date": "2026-09-10",
  "authority": "explicit_user_approval_20260910",
  "product_version": 4,
  "scope": "Name frozen Z as V4, publish Hugging Face and deploy Kaggle manual web/API test",
  "accepted_exception": "W changed 134/149 (89.93%); 13 rejected and 2 unresolved",
  "fresh_X_r2_AA_AB": "not run",
  "simulation_all_dimensions_pass": false,
  "no_new_training": true,
  "public_release_approved": true
}

	{
	"date": "2026-09-10",
	"authority": "explicit_user_approval_20260910",
	"product_version": 4,
	"scope": "Name frozen Z as V4, publish Hugging Face and deploy Kaggle manual web/API test",
	"accepted_exception": "W changed 134/149 (89.93%); 13 rejected and 2 unresolved",
	"fresh_X_r2_AA_AB": "not run",
	"simulation_all_dimensions_pass": false,
	"no_new_training": true,
	"public_release_approved": true
	}