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", device_map="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

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#!/bin/bash

# Deploy Myanmar Ghost model

set -e

echo "🚀 Starting model deployment..."

# Parse arguments
MODEL_PATH=${1:-"outputs/models/best_model.pt"}
OUTPUT_DIR=${2:-"outputs/deployment"}
FORMAT=${3:-"safetensors"}
HUB_REPO=${4:-""}

# Activate virtual environment if exists
if [ -d "venv" ]; then
    source venv/bin/activate
fi

# Install deployment dependencies
pip install -q safetensors huggingface_hub

# Run deployment pipeline
python -m src.pipelines.deployment_pipeline \
    --model_path "$MODEL_PATH" \
    --output_dir "$OUTPUT_DIR" \
    --format "$FORMAT" \
    ${HUB_REPO:+--push_hub --repo_id "$HUB_REPO"}

echo "✅ Deployment complete!"
echo "Model exported to: $OUTPUT_DIR"

if [ -n "$HUB_REPO" ]; then
    echo "Pushed to: https://huggingface.co/$HUB_REPO"
fi