Instructions to use ningpy/brunei-malayu-translator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ningpy/brunei-malayu-translator with Transformers:

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

pipe = pipeline("text-generation", model="ningpy/brunei-malayu-translator")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ningpy/brunei-malayu-translator")
model = AutoModelForCausalLM.from_pretrained("ningpy/brunei-malayu-translator", device_map="auto")
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 ningpy/brunei-malayu-translator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ningpy/brunei-malayu-translator

SGLang

How to use ningpy/brunei-malayu-translator 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 "ningpy/brunei-malayu-translator" \
    --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": "ningpy/brunei-malayu-translator",
		"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 "ningpy/brunei-malayu-translator" \
        --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": "ningpy/brunei-malayu-translator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ningpy/brunei-malayu-translator with Docker Model Runner:
```
docker model run hf.co/ningpy/brunei-malayu-translator
```

Brunei Malay -> Standard Malay Translator v3 (medical-enhanced)

Fine-tuned from Qwen3-0.6B on 29.8k Brunei-Malay <-> Standard-Malay sentence pairs. v3 adds ~1,660 medical/clinical dialect terms curated from a Brunei health-domain wordlist.

Evaluation (held-out test set, 1,623 examples)

tag	BLEU	chrF++	n
overall	60.57	79.53	1,623
sent_adversarial	77.75	85.76	348
sent_direct	54.21	76.39	1,093
sent_medical ✨	51.27	89.14	166
word_direct	0.00	42.47	15

Improvements over v2: overall BLEU +2.7, adversarial BLEU +6.5, medical chrF++ 89.14 (new).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = 'ningpy/brunei-malayu-translator'
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map='auto', trust_remote_code=True).eval()

msgs = [
    {'role': 'system', 'content': 'Translate the following Brunei Malay text into Standard Malay.'},
    {'role': 'user', 'content': 'Ku ada basal bah, sudah dua hari.'},
]
text = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tok(text, return_tensors='pt').to(model.device)
out = model.generate(**inputs, max_new_tokens=128, do_sample=False)
print(tok.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
# -> 'Saya ada selesema, sudah dua hari.'

Training

Base: Qwen/Qwen3-0.6B (Instruct)
Method: LoRA rank 32, alpha 64, targets q/k/v/o/gate/up/down (20.2M trainable, 3.4%)
Data: 29,789 train (with sent_medical upsampled 3x), 1,308 val, 1,623 test
6 epochs, lr 2e-4 cosine, effective batch 16, fp16, 1x V100 32GB, ~1h 55min
Final eval_loss: 0.2698

Data sources

General: brunei_dict (5,352-word bilingual dictionary)
Medical: bmb-dialect-medical-terms-500-v1.xlsx (500 curated medical/clinical terms with variants)
Synthesis: OpenAI gpt-4o-mini for sentence-level expansion
QA: PRPM (prpm.dbp.gov.my) cross-check, LLM-as-judge (gpt-4o)

Limitations

Test set drawn from same synthesis distribution as training; real-world BLEU may be lower
Rare Brunei words (appearing < 3 times in training) may be mistranslated
No SM -> BM reverse direction

Downloads last month: 472

Safetensors

Model size

0.6B params

Tensor type

BF16

Model tree for ningpy/brunei-malayu-translator

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Adapter

(562)

this model