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
| set -euo pipefail | |
| RUN_ID="${RUN_ID:-}" | |
| JOB_ROOT="${JOB_ROOT:-/home/student/YouZheng/jobs/taotern}" | |
| if [[ -z "$RUN_ID" ]]; then | |
| echo "RUN_ID is required" >&2 | |
| exit 2 | |
| fi | |
| safe_run_id="$(printf '%s' "$RUN_ID" | tr -c 'A-Za-z0-9_.-' '_')" | |
| job_dir="${JOB_ROOT%/}/${safe_run_id}" | |
| if [[ ! -d "$job_dir" ]]; then | |
| echo "Job directory not found: $job_dir" >&2 | |
| exit 1 | |
| fi | |
| echo "== status.json ==" | |
| cat "$job_dir/status.json" 2>/dev/null || true | |
| echo | |
| echo "== markers ==" | |
| ls -1 "$job_dir"/DONE "$job_dir"/FAILED 2>/dev/null || true | |
| echo | |
| echo "== tmux ==" | |
| tmux ls 2>/dev/null | grep -F "taotern_${safe_run_id}" || true | |
| echo | |
| echo "== recent log ==" | |
| tail -n "${TAIL_LINES:-80}" "$job_dir/train.log" 2>/dev/null || true | |
| echo | |
| echo "== outputs ==" | |
| find "$job_dir/outputs" -maxdepth 2 -type f 2>/dev/null | sort | tail -n 40 || true | |
| echo | |
| echo "== checkpoints ==" | |
| find "$job_dir/checkpoints" -maxdepth 1 -type f 2>/dev/null | sort | tail -n 20 || true | |