Instructions to use munyew/meralion-singlish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use munyew/meralion-singlish with PEFT:

from peft import PeftModel
from transformers import AutoModelForSeq2SeqLM

base_model = AutoModelForSeq2SeqLM.from_pretrained("MERaLiON/MERaLiON-2-3B")
model = PeftModel.from_pretrained(base_model, "munyew/meralion-singlish")

Transformers

How to use munyew/meralion-singlish with Transformers:

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

pipe = pipeline("text-generation", model="munyew/meralion-singlish")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("munyew/meralion-singlish", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use munyew/meralion-singlish with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/munyew/meralion-singlish

SGLang

How to use munyew/meralion-singlish 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 "munyew/meralion-singlish" \
    --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": "munyew/meralion-singlish",
		"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 "munyew/meralion-singlish" \
        --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": "munyew/meralion-singlish",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use munyew/meralion-singlish with Docker Model Runner:
```
docker model run hf.co/munyew/meralion-singlish
```

meralion-singlish / preprocessor_config.json

munyew

NSC QLoRA adapter — Singlish fine-tune

2f69b8a verified 30 days ago

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	{
	"auto_map": {
	"AutoProcessor": "MERaLiON/MERaLiON-2-3B--processing_meralion2.MERaLiON2Processor"
	},
	"chunk_length": 30,
	"dither": 0.0,
	"feature_extractor_type": "WhisperFeatureExtractor",
	"feature_size": 128,
	"hop_length": 160,
	"n_fft": 400,
	"n_samples": 480000,
	"nb_max_frames": 3000,
	"padding_side": "right",
	"padding_value": 0.0,
	"processor_class": "MERaLiON2Processor",
	"return_attention_mask": false,
	"sampling_rate": 16000
	}