Instructions to use Afeefzeed/Qwen2.5-Singlish-Transliteration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Afeefzeed/Qwen2.5-Singlish-Transliteration with Transformers:

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

pipe = pipeline("text-generation", model="Afeefzeed/Qwen2.5-Singlish-Transliteration")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Afeefzeed/Qwen2.5-Singlish-Transliteration", device_map="auto")

PEFT
How to use Afeefzeed/Qwen2.5-Singlish-Transliteration with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Afeefzeed/Qwen2.5-Singlish-Transliteration with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Afeefzeed/Qwen2.5-Singlish-Transliteration"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Afeefzeed/Qwen2.5-Singlish-Transliteration",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Afeefzeed/Qwen2.5-Singlish-Transliteration

SGLang

How to use Afeefzeed/Qwen2.5-Singlish-Transliteration 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 "Afeefzeed/Qwen2.5-Singlish-Transliteration" \
    --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": "Afeefzeed/Qwen2.5-Singlish-Transliteration",
		"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 "Afeefzeed/Qwen2.5-Singlish-Transliteration" \
        --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": "Afeefzeed/Qwen2.5-Singlish-Transliteration",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Afeefzeed/Qwen2.5-Singlish-Transliteration with Docker Model Runner:
```
docker model run hf.co/Afeefzeed/Qwen2.5-Singlish-Transliteration
```

Qwen2.5-1.5B-Singlish-Transliteration

Model Summary

Qwen2.5-1.5B-Singlish-Transliteration is a fine-tuned version of the Qwen/Qwen2.5-1.5B-Instruct large language model, specialized for transliterating Singlish (phonetic Sinhala typed in English) into Sinhala script.

This model was developed to bridge the gap between informal Romanized typing and formal Sinhala script, particularly for social media content, chat logs, and digital communication in Sri Lanka.

Developed by: Afeef Zeed
Task: Singlish to Sinhala Transliteration
Base Model: Qwen 2.5 (1.5B Parameters)
Fine-Tuning Technique: LoRA (Low-Rank Adaptation) via PEFT
Dataset Size: ~500,000 pairs of Singlish-Sinhala text

Uses

Direct Use

The model is designed to take a Singlish sentence as input and output the corresponding Sinhala text. It understands context better than rule-based transliterators.

Example:

Input: "oyage nama mokakda"
Output: "ඔයාගේ නම මොකක්ද"

How to Get Started with the Model

You can use this model directly with the Hugging Face transformers library.

Python Code

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# 1. Load Base Model
base_model_name = "Qwen/Qwen2.5-1.5B-Instruct"
adapter_model_name = "Afeefzeed/Qwen2.5-Singlish-Transliteration" # Replace with your actual username if different

print("Loading model...")
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True
)

# 2. Load the Fine-Tuned Adapter
model = PeftModel.from_pretrained(base_model, adapter_model_name)
model.eval()

# 3. Define Transliteration Function
def transliterate(text):
    prompt = f"<|im_start|>user\nTransliterate this Singlish text to Sinhala: {text}<|im_end|>\n<|im_start|>assistant\n"
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs, 
            max_new_tokens=100, 
            temperature=0.1, 
            do_sample=True
        )
    
    # Decode and extract only the assistant's response
    full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return full_output.split("assistant\n")[-1].strip()

# 4. Test
input_text = "mama heta gedara yanawa"
print(f"Input: {input_text}")
print(f"Output: {transliterate(input_text)}")

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for Afeefzeed/Qwen2.5-Singlish-Transliteration

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

(1307)

this model