Instructions to use DatarrX/myX-TransStyle-S2W with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DatarrX/myX-TransStyle-S2W with Transformers:

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

pipe = pipeline("text-generation", model="DatarrX/myX-TransStyle-S2W")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("DatarrX/myX-TransStyle-S2W")
model = AutoModelForSeq2SeqLM.from_pretrained("DatarrX/myX-TransStyle-S2W", device_map="auto")

PEFT
How to use DatarrX/myX-TransStyle-S2W with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DatarrX/myX-TransStyle-S2W with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DatarrX/myX-TransStyle-S2W"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DatarrX/myX-TransStyle-S2W",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DatarrX/myX-TransStyle-S2W

SGLang

How to use DatarrX/myX-TransStyle-S2W 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 "DatarrX/myX-TransStyle-S2W" \
    --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": "DatarrX/myX-TransStyle-S2W",
		"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 "DatarrX/myX-TransStyle-S2W" \
        --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": "DatarrX/myX-TransStyle-S2W",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DatarrX/myX-TransStyle-S2W with Docker Model Runner:
```
docker model run hf.co/DatarrX/myX-TransStyle-S2W
```

kalixlouiis commited on Apr 26

Commit

b385467

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1 Parent(s): 67e6552

Update README.md

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@@ -120,6 +120,14 @@ The model was evaluated on **100 unseen test sentences** across multiple metrics
 ### Qualitative Analysis
 Manual review by native speakers confirms that the model excels at swapping spoken particles (e.g., *...တာပါ။*) for formal equivalents (e.g., *...ခြင်းဖြစ်သည်။*). Even when the model deviates from the reference text, the outputs remain linguistically acceptable and natural within a formal context.
 ---

 ### Qualitative Analysis
 Manual review by native speakers confirms that the model excels at swapping spoken particles (e.g., *...တာပါ။*) for formal equivalents (e.g., *...ခြင်းဖြစ်သည်။*). Even when the model deviates from the reference text, the outputs remain linguistically acceptable and natural within a formal context.
+---
+## 🔗 Related Models in the DatarrX Ecosystem
+To get the most out of Myanmar Style Transfer, we recommend using these sibling models:
+* **[myX-TransStyle-W2S](https://huggingface.co/DatarrX/myX-TransStyle-W2S):** The inverse model for converting Written Style to Spoken Style.
+* **[myX-StyleClassifier](https://huggingface.co/DatarrX/myX-StyleClassifier):** A high-performance classifier to identify whether a sentence is Written or Spoken before applying style transfer.
 ---