Instructions to use ramshafirdous/malaysian-address-corrector-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ramshafirdous/malaysian-address-corrector-lora with Transformers:

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

pipe = pipeline("text-generation", model="ramshafirdous/malaysian-address-corrector-lora")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ramshafirdous/malaysian-address-corrector-lora", dtype="auto")

PEFT
How to use ramshafirdous/malaysian-address-corrector-lora with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ramshafirdous/malaysian-address-corrector-lora with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ramshafirdous/malaysian-address-corrector-lora

SGLang

How to use ramshafirdous/malaysian-address-corrector-lora 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 "ramshafirdous/malaysian-address-corrector-lora" \
    --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": "ramshafirdous/malaysian-address-corrector-lora",
		"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 "ramshafirdous/malaysian-address-corrector-lora" \
        --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": "ramshafirdous/malaysian-address-corrector-lora",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ramshafirdous/malaysian-address-corrector-lora with Docker Model Runner:
```
docker model run hf.co/ramshafirdous/malaysian-address-corrector-lora
```

ramshafirdous commited on Aug 11, 2025

Commit

af4e04a

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1 Parent(s): e5c1dd5

Update README.md

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@@ -5,16 +5,8 @@ library_name: transformers
 pipeline_tag: text-generation
 model_type: peft
 adapter_type: lora
-language:
-  - en
-  - ms
-tags:
-  - peft
-  - lora
-  - qlora
-  - address-normalization
-  - address-correction
-  - malaysia
 ---
 # Malaysian Address Corrector LoRA

 pipeline_tag: text-generation
 model_type: peft
 adapter_type: lora
+language: [en, ms]
+tags: [peft, lora, qlora, address-normalization, address-correction, malaysia]
 ---
 # Malaysian Address Corrector LoRA