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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e2bfccc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | #!/usr/bin/env bash
set -euo pipefail
RUN_ID="${RUN_ID:-taotern-pre-200m-stability-gate-$(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}"
TARGET_TOKENS="${TARGET_TOKENS:-20000000}"
MAX_TOKENS="${MAX_TOKENS:-50000000}"
MAX_RECORDS="${MAX_RECORDS:-120000}"
EVAL_BATCHES="${EVAL_BATCHES:-64}"
LEARNING_RATE="${LEARNING_RATE:-0.0008}"
WEIGHT_DECAY="${WEIGHT_DECAY:-0.01}"
TRAIN_LOG_EVERY="${TRAIN_LOG_EVERY:-250}"
SFT_SANITY_SAMPLES="${SFT_SANITY_SAMPLES:-4}"
SFT_SANITY_STEPS="${SFT_SANITY_STEPS:-120}"
SFT_SANITY_LR="${SFT_SANITY_LR:-0.00005}"
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 TARGET_TOKENS=$TARGET_TOKENS MAX_TOKENS=$MAX_TOKENS MAX_RECORDS=$MAX_RECORDS EVAL_BATCHES=$EVAL_BATCHES LEARNING_RATE=$LEARNING_RATE WEIGHT_DECAY=$WEIGHT_DECAY TRAIN_LOG_EVERY=$TRAIN_LOG_EVERY SFT_SANITY_SAMPLES=$SFT_SANITY_SAMPLES SFT_SANITY_STEPS=$SFT_SANITY_STEPS SFT_SANITY_LR=$SFT_SANITY_LR bash scripts/remote/run_pre_200m_stability_gate.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
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