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", 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
| # Transformer Model Configuration | |
| model: | |
| name: myanmar_ghost_transformer | |
| type: transformer | |
| architecture: custom | |
| # Model architecture | |
| architecture: | |
| hidden_size: 768 | |
| num_layers: 12 | |
| num_heads: 12 | |
| intermediate_size: 3072 | |
| dropout: 0.1 | |
| attention_dropout: 0.1 | |
| # Vocab | |
| vocab: | |
| type: sentencepiece | |
| vocab_size: 32000 | |
| special_tokens: | |
| pad: "<pad>" | |
| unk: "<unk>" | |
| bos: "<s>" | |
| eos: "</s>" | |
| # Training | |
| training: | |
| batch_size: 16 | |
| learning_rate: 5e-5 | |
| weight_decay: 0.01 | |
| adam_beta1: 0.9 | |
| adam_beta2: 0.999 | |
| gradient_accumulation_steps: 4 | |
| max_grad_norm: 1.0 | |
| num_epochs: 10 | |
| warmup_steps: 500 | |
| scheduler: linear | |
| # Mixed precision | |
| mixed_precision: true | |
| fp16: true | |
| # Regularization | |
| regularization: | |
| dropout: 0.1 | |
| label_smoothing: 0.1 | |
| early_stopping: | |
| enabled: true | |
| patience: 3 | |
| monitor: val_loss | |