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
| set -euo pipefail | |
| ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| TORCH_FLAVOR="${1:-default}" | |
| python3 -m venv "$ROOT/.venv" | |
| "$ROOT/.venv/bin/python" -m pip install --upgrade pip setuptools wheel | |
| case "$TORCH_FLAVOR" in | |
| cpu) | |
| "$ROOT/.venv/bin/python" -m pip install torch --index-url https://download.pytorch.org/whl/cpu | |
| ;; | |
| cu121|cu124|cu126|cu128) | |
| "$ROOT/.venv/bin/python" -m pip install torch --index-url "https://download.pytorch.org/whl/$TORCH_FLAVOR" | |
| ;; | |
| default) | |
| "$ROOT/.venv/bin/python" -m pip install torch | |
| ;; | |
| *) | |
| echo "Unsupported torch flavor: $TORCH_FLAVOR" >&2 | |
| echo "Use one of: default cpu cu121 cu124 cu126 cu128" >&2 | |
| exit 2 | |
| ;; | |
| esac | |
| "$ROOT/.venv/bin/python" -m pip install -e "$ROOT/code/Taotern_SSM" | |
| "$ROOT/.venv/bin/python" -m pip install -e "$ROOT/code/TaoTrain" | |
| echo | |
| echo "Setup complete." | |
| "$ROOT/.venv/bin/python" - <<'PY' | |
| import torch | |
| print("torch:", torch.__version__) | |
| print("cuda available:", torch.cuda.is_available()) | |
| if torch.cuda.is_available(): | |
| print("cuda device:", torch.cuda.get_device_name(0)) | |
| PY | |
| echo | |
| echo "Run fixed chat with:" | |
| echo " ./run_chat_fixed.sh" | |