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 | |
| DATA_PATH="${DATA_PATH:-/home/student/Data/TaoData/pretrain.jsonl}" | |
| TOKENIZER_PATH="${TOKENIZER_PATH:-/home/student/YouZheng/tokenizers/taodata_pilot_8k/tokenizer.model}" | |
| SSM_REPO_PATH="${SSM_REPO_PATH:-/home/student/YouZheng/gamma_ssm_repo}" | |
| PYTHON_BIN="${PYTHON_BIN:-/home/student/.venv/bin/python}" | |
| REMOTE_REPO="${REMOTE_REPO:-$(pwd)}" | |
| OUTPUT_BASE="${REPOBRIDGE_OUTPUT_DIR:-$REMOTE_REPO/results/200m-base-suite}" | |
| CHECKPOINT_BASE="${TAOTERN_CHECKPOINT_DIR:-$OUTPUT_BASE/checkpoints}" | |
| # Stage-1 defaults are intentionally modest. Increase these through environment | |
| # variables after the 200M shapes are stable on the RTX5090. | |
| MAX_TOKENS="${MAX_TOKENS:-50000000}" | |
| MAX_RECORDS="${MAX_RECORDS:-100000}" | |
| TRAIN_STEPS="${TRAIN_STEPS:-200}" | |
| EVAL_BATCHES="${EVAL_BATCHES:-16}" | |
| BATCH_SIZES="${BATCH_SIZES:-4,8}" | |
| SEQ_LEN="${SEQ_LEN:-512}" | |
| LEARNING_RATE="${LEARNING_RATE:-0.0006}" | |
| WEIGHT_DECAY="${WEIGHT_DECAY:-0.01}" | |
| DRY_RUN="${DRY_RUN:-0}" | |
| export PYTHONPATH="$REMOTE_REPO/src:$SSM_REPO_PATH" | |
| mkdir -p "$OUTPUT_BASE" "$CHECKPOINT_BASE" | |
| run_variant() { | |
| local variant="$1" | |
| shift | |
| local output_dir="$OUTPUT_BASE/$variant" | |
| local checkpoint_dir="$CHECKPOINT_BASE/$variant" | |
| mkdir -p "$output_dir" "$checkpoint_dir" | |
| local cmd="$PYTHON_BIN scripts/benchmark_taonet_real_tokens.py \ | |
| --data-path $DATA_PATH \ | |
| --text-field text \ | |
| --tokenizer-type sentencepiece \ | |
| --tokenizer-path $TOKENIZER_PATH \ | |
| --max-records $MAX_RECORDS \ | |
| --max-tokens $MAX_TOKENS \ | |
| --eval-fraction 0.1 \ | |
| --batch-sizes $BATCH_SIZES \ | |
| --seq-len $SEQ_LEN \ | |
| --dtype bf16 \ | |
| --device cuda \ | |
| --warmup 1 \ | |
| --repeats 2 \ | |
| --backward \ | |
| --train-steps $TRAIN_STEPS \ | |
| --learning-rate $LEARNING_RATE \ | |
| --weight-decay $WEIGHT_DECAY \ | |
| --eval-batches $EVAL_BATCHES \ | |
| --output-dir $output_dir \ | |
| --resume-completed \ | |
| --incremental-output \ | |
| --save-case-checkpoints \ | |
| --checkpoint-dir $checkpoint_dir \ | |
| $*" | |
| printf '\n=== 200M variant: %s ===\n' "$variant" | |
| printf '%s\n' "$cmd" | |
| if [ "$DRY_RUN" = "1" ]; then | |
| return 0 | |
| fi | |
| eval "$cmd" | |
| } | |
| run_variant attention_196m \ | |
| --architectures taonet \ | |
| --hidden-dim 960 \ | |
| --num-layers 16 \ | |
| --num-heads 8 \ | |
| --d-latent-kv 720 \ | |
| --d-rope 120 \ | |
| --hidden-dim-ff 2880 | |
| run_variant pure_ssm_196m_hadamard \ | |
| --architectures taonet_ssm \ | |
| --hidden-dim 1024 \ | |
| --num-layers 18 \ | |
| --num-heads 8 \ | |
| --d-latent-kv 768 \ | |
| --d-rope 128 \ | |
| --hidden-dim-ff 3072 \ | |
| --ssm-core dplr \ | |
| --ssm-hidden-dims 16 \ | |
| --ssm-mixer-dims 256 \ | |
| --ssm-num-lanes-list 2 \ | |
| --ssm-lane-combine channel \ | |
| --ssm-lane-modes split \ | |
| --ssm-split-mixes hadamard \ | |
| --ssm-rank 1 \ | |
| --ssm-kernel-mode conv \ | |
| --no-ssm-finite-tail-correction \ | |
| --ssm-gate-types channel \ | |
| --ssm-local-shift \ | |
| --ssm-local-shift-per-channel \ | |
| --ssm-local-shift-init 0.1 | |
| run_variant pure_ssm_196m_nomix \ | |
| --architectures taonet_ssm \ | |
| --hidden-dim 1024 \ | |
| --num-layers 18 \ | |
| --num-heads 8 \ | |
| --d-latent-kv 768 \ | |
| --d-rope 128 \ | |
| --hidden-dim-ff 3072 \ | |
| --ssm-core dplr \ | |
| --ssm-hidden-dims 16 \ | |
| --ssm-mixer-dims 256 \ | |
| --ssm-num-lanes-list 2 \ | |
| --ssm-lane-combine channel \ | |
| --ssm-lane-modes split \ | |
| --ssm-split-mixes none \ | |
| --ssm-rank 1 \ | |
| --ssm-kernel-mode conv \ | |
| --no-ssm-finite-tail-correction \ | |
| --ssm-gate-types channel \ | |
| --ssm-local-shift \ | |
| --ssm-local-shift-per-channel \ | |
| --ssm-local-shift-init 0.1 | |
| run_variant hybrid_ssm_first_199m \ | |
| --architectures taonet_hybrid \ | |
| --hidden-dim 1024 \ | |
| --num-layers 16 \ | |
| --num-heads 8 \ | |
| --d-latent-kv 768 \ | |
| --d-rope 128 \ | |
| --hidden-dim-ff 3072 \ | |
| --ssm-core dplr \ | |
| --ssm-hidden-dims 32 \ | |
| --ssm-mixer-dims 256 \ | |
| --ssm-num-lanes-list 2 \ | |
| --ssm-lane-combine channel \ | |
| --ssm-lane-modes split \ | |
| --ssm-split-mixes hadamard \ | |
| --ssm-rank 1 \ | |
| --ssm-kernel-mode conv \ | |
| --no-ssm-finite-tail-correction \ | |
| --ssm-gate-types channel \ | |
| --hybrid-patterns ssm_first \ | |
| --ssm-local-shift \ | |
| --ssm-local-shift-per-channel \ | |
| --ssm-local-shift-init 0.1 | |
| if [ "$DRY_RUN" != "1" ]; then | |
| "$PYTHON_BIN" scripts/summarize_taonet_benchmark_suite.py --suite-dir "$OUTPUT_BASE" | |
| fi | |