Instructions to use amkyawdev/myanmar-ghost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amkyawdev/myanmar-ghost with Transformers:

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amkyawdev/myanmar-ghost", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use amkyawdev/myanmar-ghost with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/amkyawdev/myanmar-ghost

SGLang

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

Docker Model Runner
How to use amkyawdev/myanmar-ghost with Docker Model Runner:
```
docker model run hf.co/amkyawdev/myanmar-ghost
```

myanmar-ghost / README_my.md

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🇲🇲 Myanmar Ghost

မိတ်ဆက်

Myanmar Ghost သည် မြန်မာစာသားအနက်ဖွင့်ဆိုခြင်း (Sentiment Analysis) အတွက် အဆင့်မြင့် AI Model တစ်ခုဖြစ်ပါသည်။

အဓိကလုပ်ဆောင်ချက်များ

Multi-Modal Fusion - အသံ + စာသား ပေါင်းစပ်ခြင်း
Active Learning - ထိရောက်သော အညွှန်းသတ်မှတ်ခြင်း
Federated Learning - Privacy-preserving training
Explainable AI - SHAP/LIME ဖြင့် ရှင်းပြခြင်း
Data Augmentation - Adversarial augmentation

တပ်ဆင်ခြင်း

pip install -r requirements.txt

အသုံးပြုပုံ

Training

python src/models/train.py \
    --train_data data/processed/splits/train.csv \
    --output_dir outputs/models

Evaluation

python src/models/evaluate.py \
    --model_path outputs/models/best_model.pt \
    --data_path data/processed/splits/test.csv

API Run

uvicorn deployment.api.app:app --host 0.0.0.0 --port 8000

Project Structure

Myanmar-Ghost/
├── src/
│   ├── data_processing/     # Data processing
│   ├── annotation/          # Annotation tools
│   ├── models/             # Model implementations
│   ├── federated/          # Federated learning
│   ├── xai/               # Explainable AI
│   ├── augmentation/        # Data augmentation
│   └── utils/             # Utilities
├── configs/                 # Configuration
├── data/                    # Data directory
├── tests/                   # Unit tests
└── docs/                    # Documentation

License

Apache 2.0

Author

Aung Myo Kyaw
https://huggingface.co/amkyawdev