Text Generation
Transformers
Burmese
English
myanmar
burmese
llm
chat
instruction-following
conversational
autoregressive
Instructions to use amkyawdev/myanmar-ghost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| # Download sample data for Myanmar Ghost project | |
| set -e | |
| echo "π₯ Downloading sample data..." | |
| # Create data directories | |
| mkdir -p data/raw/audio | |
| mkdir -p data/raw/transcripts | |
| mkdir -p data/processed | |
| # Download sample audio (placeholder - replace with actual URLs) | |
| # Example: wget -O data/raw/audio/sample.wav "https://example.com/sample.wav" | |
| # Create sample metadata | |
| cat > data/raw/metadata.csv << 'EOF' | |
| id,audio_file,transcript,sentiment | |
| utt_001,session_001/speaker_a.wav,αααΊαΉααα¬αα«,neutral | |
| utt_002,session_001/speaker_b.wav,αα»α±αΈαα°αΈαα«,positive | |
| utt_003,session_002/speaker_a.wav,ααα»α±αααΊαα«αα»,negative | |
| EOF | |
| # Download sample from HuggingFace datasets (if datasets is installed) | |
| if command -v python &> /dev/null; then | |
| python -c " | |
| from datasets import load_dataset | |
| # Load a sample dataset | |
| print('Sample data structure created successfully') | |
| " | |
| fi | |
| echo "β Sample data downloaded to data/raw/" | |
| echo "" | |
| echo "Next steps:" | |
| echo " 1. Add your actual audio files to data/raw/audio/" | |
| echo " 2. Update transcripts in data/raw/metadata.csv" | |
| echo " 3. Run: bash scripts/run_data_pipeline.sh" | |