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
File size: 912 Bytes
388fd6e | 1 2 3 4 5 6 7 | #!/usr/bin/env bash
set -euo pipefail
export REPOBRIDGE_OUTPUT_DIR="/home/student/YouZheng/jobs/taotern/taotern-200m-branch-only-chat-20260514/outputs"
export TAOTERN_CHECKPOINT_DIR="/home/student/YouZheng/jobs/taotern/taotern-200m-branch-only-chat-20260514/checkpoints"
cd "/home/student/YouZheng/repo"
REMOTE_REPO=/home/student/YouZheng/repo PYTHON_BIN=/home/student/.venv/bin/python SSM_REPO_PATH=/home/student/YouZheng/gamma_ssm_repo DATA_PATH=/home/student/Data/TaoData/pretrain.jsonl SFT_DATA_PATH=/home/student/Data/TaoData/sft.jsonl TOKENIZER_PATH=/home/student/YouZheng/tokenizers/taodata_pilot_8k/tokenizer.model SEQ_LEN=512 BATCH_SIZE=8 PRETRAIN_TOKENS=4000000000 SFT_STEPS=50000 PRETRAIN_LR=0.0008 SFT_LR=0.00005 WEIGHT_DECAY=0.01 LOG_EVERY=100 SAVE_EVERY=100000 SFT_SAVE_EVERY=10000 TOKENIZER_THREADS=8 SAMPLES_PER_CHUNK=2000 BLOCK_RESIDUAL_RMS_CAP= bash scripts/remote/run_200m_branch_only_chat.sh
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