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="${RUN_ID:-taotern-200m-branch-only-chat-$(date +%Y%m%d-%H%M%S)}" | |
| JOB_ROOT="${JOB_ROOT:-/home/student/YouZheng/jobs/taotern}" | |
| REMOTE_REPO="${REMOTE_REPO:-$(pwd)}" | |
| PYTHON_BIN="${PYTHON_BIN:-/home/student/.venv/bin/python}" | |
| SSM_REPO_PATH="${SSM_REPO_PATH:-/home/student/YouZheng/gamma_ssm_repo}" | |
| DATA_PATH="${DATA_PATH:-/home/student/Data/TaoData/pretrain.jsonl}" | |
| SFT_DATA_PATH="${SFT_DATA_PATH:-/home/student/Data/TaoData/sft.jsonl}" | |
| TOKENIZER_PATH="${TOKENIZER_PATH:-/home/student/YouZheng/tokenizers/taodata_pilot_8k/tokenizer.model}" | |
| SEQ_LEN="${SEQ_LEN:-512}" | |
| BATCH_SIZE="${BATCH_SIZE:-8}" | |
| PRETRAIN_TOKENS="${PRETRAIN_TOKENS:-4000000000}" | |
| SFT_STEPS="${SFT_STEPS:-50000}" | |
| PRETRAIN_LR="${PRETRAIN_LR:-0.0008}" | |
| SFT_LR="${SFT_LR:-0.00005}" | |
| WEIGHT_DECAY="${WEIGHT_DECAY:-0.01}" | |
| LOG_EVERY="${LOG_EVERY:-100}" | |
| SAVE_EVERY="${SAVE_EVERY:-100000}" | |
| SFT_SAVE_EVERY="${SFT_SAVE_EVERY:-10000}" | |
| TOKENIZER_THREADS="${TOKENIZER_THREADS:-8}" | |
| SAMPLES_PER_CHUNK="${SAMPLES_PER_CHUNK:-2000}" | |
| BLOCK_RESIDUAL_RMS_CAP="${BLOCK_RESIDUAL_RMS_CAP:-}" | |
| JOB_COMMAND="REMOTE_REPO=$REMOTE_REPO PYTHON_BIN=$PYTHON_BIN SSM_REPO_PATH=$SSM_REPO_PATH DATA_PATH=$DATA_PATH SFT_DATA_PATH=$SFT_DATA_PATH TOKENIZER_PATH=$TOKENIZER_PATH SEQ_LEN=$SEQ_LEN BATCH_SIZE=$BATCH_SIZE PRETRAIN_TOKENS=$PRETRAIN_TOKENS SFT_STEPS=$SFT_STEPS PRETRAIN_LR=$PRETRAIN_LR SFT_LR=$SFT_LR WEIGHT_DECAY=$WEIGHT_DECAY LOG_EVERY=$LOG_EVERY SAVE_EVERY=$SAVE_EVERY SFT_SAVE_EVERY=$SFT_SAVE_EVERY TOKENIZER_THREADS=$TOKENIZER_THREADS SAMPLES_PER_CHUNK=$SAMPLES_PER_CHUNK BLOCK_RESIDUAL_RMS_CAP=$BLOCK_RESIDUAL_RMS_CAP bash scripts/remote/run_200m_branch_only_chat.sh" | |
| export RUN_ID JOB_ROOT JOB_COMMAND | |
| export OUTPUT_DIR="${OUTPUT_DIR:-$JOB_ROOT/$RUN_ID/outputs}" | |
| export CHECKPOINT_DIR="${CHECKPOINT_DIR:-$JOB_ROOT/$RUN_ID/checkpoints}" | |
| bash scripts/remote/submit_detached_job.sh | |