Text Generation
Transformers
PyTorch
English
taonet_mini_t2
taonet
taotern
ssm
state-space-model
dplr
custom_code
experimental
Instructions to use TaoTern/TaoNet-mini-T2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TaoTern/TaoNet-mini-T2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TaoTern/TaoNet-mini-T2", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TaoTern/TaoNet-mini-T2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TaoTern/TaoNet-mini-T2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TaoTern/TaoNet-mini-T2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TaoTern/TaoNet-mini-T2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TaoTern/TaoNet-mini-T2
- SGLang
How to use TaoTern/TaoNet-mini-T2 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 "TaoTern/TaoNet-mini-T2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TaoTern/TaoNet-mini-T2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "TaoTern/TaoNet-mini-T2" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TaoTern/TaoNet-mini-T2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TaoTern/TaoNet-mini-T2 with Docker Model Runner:
docker model run hf.co/TaoTern/TaoNet-mini-T2
| """Optimizer registry and factory for instantiating optimizers.""" | |
| from typing import Dict, Type, Callable, Any | |
| import torch.optim as optim | |
| from taoTrain.core.base import BaseModel | |
| from taoTrain.config import TrainingConfig, OptimizerEnum | |
| # Global registry for optimizers | |
| _OPTIMIZER_REGISTRY: Dict[str, Callable] = {} | |
| def register_optimizer(name: str): | |
| """ | |
| Decorator to register a custom optimizer factory function. | |
| Args: | |
| name: Name of the optimizer (e.g., 'adamw', 'adam', 'sgd') | |
| """ | |
| def decorator(fn: Callable) -> Callable: | |
| if name in _OPTIMIZER_REGISTRY: | |
| raise ValueError(f"Optimizer '{name}' is already registered") | |
| _OPTIMIZER_REGISTRY[name] = fn | |
| return fn | |
| return decorator | |
| def get_registered_optimizers() -> Dict[str, Callable]: | |
| """Get all registered optimizer factory functions.""" | |
| return _OPTIMIZER_REGISTRY.copy() | |
| def get_optimizer( | |
| model: BaseModel, | |
| config: TrainingConfig, | |
| ) -> optim.Optimizer: | |
| """ | |
| Create an optimizer instance from config. | |
| Args: | |
| model: Model to optimize | |
| config: TrainingConfig with optimizer configuration | |
| Returns: | |
| Optimizer instance | |
| Raises: | |
| ValueError: If optimizer type is not registered | |
| """ | |
| # Handle both enum and string values | |
| optimizer_type = config.optimizer.optimizer_type | |
| if isinstance(optimizer_type, str): | |
| optimizer_name = optimizer_type | |
| else: | |
| optimizer_name = optimizer_type.value | |
| if optimizer_name not in _OPTIMIZER_REGISTRY: | |
| raise ValueError( | |
| f"Unknown optimizer: {optimizer_name}. " | |
| f"Available: {list(_OPTIMIZER_REGISTRY.keys())}" | |
| ) | |
| factory_fn = _OPTIMIZER_REGISTRY[optimizer_name] | |
| return factory_fn(model, config) | |
| def register_builtin_optimizers(): | |
| """Register all built-in optimizers.""" | |
| # Import here to trigger decorator registration (avoid circular imports) | |
| from . import adamw # noqa: F401 | |
| from . import adam # noqa: F401 | |
| from . import sgd # noqa: F401 | |
| from . import hybrid_muon_adamw # noqa: F401 | |
| # Auto-register built-in optimizers when module is imported | |
| register_builtin_optimizers() | |