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 / api_reference.md

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API Reference

FastAPI Endpoints

Health Check

GET /health

Response:

{
  "status": "healthy",
  "model_loaded": true
}

Predict Sentiment

POST /predict

Request:

{
  "text": "ကျေးဇူးပါ",
  "include_prosody": false
}

Response:

{
  "text": "ကျေးဇူးပါ",
  "sentiment": "positive",
  "confidence": 0.95,
  "probabilities": {
    "negative": 0.01,
    "neutral": 0.02,
    "positive": 0.95,
    "sarcastic": 0.02
  }
}

Batch Predict

POST /predict_batch

Request:

{
  "texts": ["ကျေးဇူးပါ", "မကျေနပ်ပါဗျ"]
}

Python SDK

Installation

pip install myanmar-ghost

Usage

from myanmar_ghost import MyanmarGhost

# Initialize
model = MyanmarGhost()

# Predict
result = model.predict("ကျေးဇူးပါ")
print(result.sentiment)  # "positive"

# Batch predict
results = model.predict_batch([
    "ကျေးဇူးပါ",
    "မကျေနပ်ပါ"
])

Advanced Usage

XAI Explanations

from myanmar_ghost.xai import SHAPExplainer

explainer = SHAPExplainer(model)
shap_values = explainer.explain("ကျေးဇူးပါ")
explainer.visualize(shap_values)

Active Learning

from myanmar_ghost.active_learning import UncertaintySampler

sampler = UncertaintySampler(model)
selected = sampler.select_samples(unlabeled_data, n_samples=100)

CLI Commands

# Train model
python -m src.models.train --train_data data/train.csv --output_dir outputs/models

# Evaluate model
python -m src.models.evaluate --model_path outputs/models/best_model.pt --data_path data/test.csv

# Deploy
bash scripts/deploy_model.sh outputs/models/best_model.pt

Configuration

Environment Variables

Variable	Description	Default
MODEL_PATH	Path to model files	outputs/models
HF_TOKEN	HuggingFace token	None
DEVICE	cuda or cpu	cuda

Model Config

model:
  name: myanmar_ghost
  hidden_size: 768
  num_layers: 12
  dropout: 0.1