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
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requires = ["setuptools>=68.0", "wheel"]
build-backend = "setuptools.build_meta"
[project]
name = "taoTrain"
version = "0.1.0"
description = "Clean, modular PyTorch LLM training framework with pluggable architectures, AimStack logging, and TUI inference"
readme = "README.md"
requires-python = ">=3.10"
license = { text = "MIT" }
authors = [
{ name = "Felix", email = "felix@example.com" }
]
dependencies = [
"torch>=2.0.0",
"transformers>=4.30.0",
"datasets>=2.10.0",
"pydantic>=2.0.0",
"pydantic-settings>=2.0.0",
"aim>=3.15.0",
"click>=8.1.0",
"rich>=13.0.0",
"textual>=0.30.0",
"numpy>=1.24.0",
"tqdm>=4.65.0",
"sentencepiece>=0.1.99",
]
[project.optional-dependencies]
dev = [
"pytest>=7.4.0",
"pytest-cov>=4.1.0",
"pytest-xdist>=3.3.0",
"black>=23.7.0",
"ruff>=0.0.280",
"typing-extensions>=4.7.0",
]
[project.scripts]
train = "taoTrain.cli:main"
train-tokenizer = "taoTrain.cli:train_tokenizer_command"
tui-chat = "taoTrain.inference.tui:main"
[tool.setuptools.packages.find]
where = ["src"]
[tool.setuptools.package-data]
taoTrain = ["configs/**/*.yaml"]
[tool.black]
line-length = 100
target-version = ["py310"]
[tool.ruff]
line-length = 100
target-version = "py310"
select = ["E", "F", "W", "I", "N", "UP", "RUF"]
ignore = ["E501"]
[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = "test_*.py"
addopts = "--verbose"
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