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
| # Myanmar Ghost Model Card | |
| ## π·οΈ Model Overview | |
| **Model Name**: Myanmar-Ghost-Instruct | |
| **Model Type**: Text Classification (Sentiment Analysis) | |
| **Language**: Myanmar (Burmese) | |
| **Version**: 1.0.0 | |
| **Last Updated**: 2025 | |
| ## π 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 | |
| ## π Training Data | |
| - **Source**: Myanmar speech datasets | |
| - **Size**: ~1M samples | |
| - **Splits**: 80% train, 10% validation, 10% test | |
| ## βοΈ Model Architecture | |
| ``` | |
| Transformer (BERT-based multilingual) | |
| βββ Hidden Size: 768 | |
| βββ Layers: 12 | |
| βββ Heads: 12 | |
| βββ Classifier Head | |
| βββ 4-class output (negative, neutral, positive, sarcastic) | |
| ``` | |
| ## π Performance | |
| | Metric | Score | | |
| |--------|-------| | |
| | Accuracy | ~92% | | |
| | F1 (weighted) | ~91% | | |
| | F1 (macro) | ~89% | | |
| | Precision | ~91% | | |
| | Recall | ~91% | | |
| ## π§ Usage | |
| ### Python | |
| ```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 = "αα»α±αΈαα°αΈαα«" | |
| inputs = tokenizer(text, return_tensors="pt") | |
| outputs = model(**inputs) | |
| ``` | |
| ### API | |
| ```bash | |
| curl -X POST http://localhost:8000/predict \ | |
| -H "Content-Type: application/json" \ | |
| -d '{"text": "αα»α±αΈαα°αΈαα«"}' | |
| ``` | |
| ## β οΈ Ethical Considerations | |
| - Model trained on publicly available Myanmar data | |
| - No personally identifiable information used | |
| - Regular evaluation for bias | |
| ## π Citation | |
| ``` | |
| @software{myanmar_ghost, | |
| title = {Myanmar Ghost}, | |
| author = {Aung Myo Kyaw}, | |
| url = {https://huggingface.co/amkyawdev/Myanmar-Ghost-Instruct}, | |
| year = {2025}, | |
| } | |
| ``` | |
| ## π€ License | |
| Apache 2.0 | |