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
- 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
| @echo off | |
| setlocal | |
| cd /d "%~dp0" | |
| echo ============================================================ | |
| echo Taotern 200M Branch-Only Chat - Windows Test Launcher | |
| echo ============================================================ | |
| echo. | |
| echo This will create/use .venv, install CUDA PyTorch, install the | |
| echo packaged TaoTrain/Taotern_SSM code, then launch fixed chat. | |
| echo. | |
| echo [1/2] Trying setup with PyTorch CUDA 12.8... | |
| powershell -NoProfile -ExecutionPolicy Bypass -File "%~dp0setup_windows.ps1" -TorchFlavor cu128 | |
| if errorlevel 1 ( | |
| echo. | |
| echo CUDA 12.8 setup failed. Trying CUDA 12.6... | |
| powershell -NoProfile -ExecutionPolicy Bypass -File "%~dp0setup_windows.ps1" -TorchFlavor cu126 | |
| if errorlevel 1 ( | |
| echo. | |
| echo CUDA setup failed. You can try CPU-only setup manually: | |
| echo powershell -ExecutionPolicy Bypass -File .\setup_windows.ps1 -TorchFlavor cpu | |
| echo. | |
| pause | |
| exit /b 1 | |
| ) | |
| ) | |
| echo. | |
| echo [2/2] Launching fixed chat... | |
| powershell -NoProfile -ExecutionPolicy Bypass -File "%~dp0run_chat_fixed.ps1" | |
| echo. | |
| pause | |