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 / model_card.md

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Myanmar Ghost Model Card

🏷️ Model Overview

Model Name: Myanmar-Ghost-Instruct
Model Type: Text Classification (Sentiment Analysis)
Language: Myanmar (Burmese)
Version: 1.0.0
Last Updated: 2025

📊 Model Description

Myanmar Ghost is an advanced sentiment analysis model for Myanmar language that classifies text into 4 sentiment categories with multi-modal capability (audio + text).

Capabilities

Myanmar text sentiment classification
Multi-modal fusion (audio prosody + text)
Explainable AI (SHAP, LIME)
Privacy-preserving (Federated Learning ready)

Limitations

Best performance on formal Myanmar text
May struggle with heavy use of emoji/emoticons
Limited performance on code-mixed text

📈 Training Data

Source: Myanmar speech datasets
Size: ~1M samples
Splits: 80% train, 10% validation, 10% test

⚙️ Model Architecture

Transformer (BERT-based multilingual)
├── Hidden Size: 768
├── Layers: 12
├── Heads: 12
└── Classifier Head
    └── 4-class output (negative, neutral, positive, sarcastic)

📉 Performance

Metric	Score
Accuracy	~92%
F1 (weighted)	~91%
F1 (macro)	~89%
Precision	~91%
Recall	~91%

🔧 Usage

Python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "amkyawdev/Myanmar-Ghost-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

# Predict
text = "ကျေးဇူးပါ"
inputs = tokenizer(text, return_tensors="pt")
outputs = model(**inputs)

API

curl -X POST http://localhost:8000/predict \
  -H "Content-Type: application/json" \
  -d '{"text": "ကျေးဇူးပါ"}'

⚠️ Ethical Considerations

Model trained on publicly available Myanmar data
No personally identifiable information used
Regular evaluation for bias

📝 Citation

@software{myanmar_ghost,
  title = {Myanmar Ghost},
  author = {Aung Myo Kyaw},
  url = {https://huggingface.co/amkyawdev/Myanmar-Ghost-Instruct},
  year = {2025},
}

🤝 License

Apache 2.0