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
| # Core ML/AI | |
| torch>=2.0.0 | |
| transformers>=4.35.0 | |
| peft>=0.7.0 | |
| accelerate>=0.25.0 | |
| datasets>=2.14.0 | |
| scikit-learn>=1.3.0 | |
| # Audio Processing | |
| librosa>=0.10.0 | |
| soundfile>=0.12.1 | |
| torchaudio>=2.0.0 | |
| scipy>=1.11.0 | |
| # Data Versioning | |
| dvc>=3.0.0 | |
| dvc-gdrive>=2.0.0 | |
| # Text Processing | |
| numpy>=1.24.0 | |
| pandas>=2.0.0 | |
| pyyaml>=6.0 | |
| regex>=2023.0.0 | |
| sentencepiece>=0.1.99 | |
| # XAI (Explainable AI) | |
| shap>=0.42.0 | |
| lime>=0.3.0 | |
| # Multi-Modal | |
| openai-whisper>=20231117 | |
| # Federated Learning | |
| flwr>=1.5.0 | |
| # Graph | |
| networkx>=3.1 | |
| rdflib>=7.0.0 | |
| # Utilities | |
| tqdm>=4.66.0 | |
| loguru>=0.7.0 | |
| hydra-core>=1.3.0 | |
| omegaconf>=2.3.0 | |
| python-dotenv>=1.0.0 | |
| # API & Deployment | |
| fastapi>=0.104.0 | |
| uvicorn>=0.24.0 | |
| pydantic>=2.0.0 | |
| gradio>=4.0.0 | |
| # Testing | |
| pytest>=7.4.0 | |
| pytest-cov>=4.1.0 | |
| pytest-asyncio>=0.21.0 | |