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
| # Directory Navigation Guide | |
| This guide maps the project by responsibility. Use it when a new thread needs to find the SSM code, LLM wrapper, data pipeline, train/test scripts, or remote-run artifacts quickly. | |
| ## Local Workspace | |
| Current local workspace root: | |
| ```text | |
| C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern | |
| ``` | |
| Main local repos: | |
| | Purpose | Local path | GitHub | | |
| |---|---|---| | |
| | Experiment ledger | `C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\Taotern_LLM_Experiments` | `https://github.com/StarMists/Taotern_LLM_Experiments` | | |
| | SSM model | `C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\Taotern_SSM` | `https://github.com/StarMists/gamma_SSM_S4_enhanced` | | |
| | TaoTrain LLM code | `C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\TaoTrain` | `https://github.com/lobakkang/TaoTrain` | | |
| | Remote run tool | `C:\Users\YouZheng\Documents\LYZ\MyContent\MyComp\RepoBridge` | local tool repo | | |
| | TaoData scripts | not currently cloned under this workspace | `https://github.com/lobakkang/TaoData` | | |
| ## Experiment Ledger | |
| Path: | |
| ```text | |
| C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\Taotern_LLM_Experiments | |
| ``` | |
| Important files: | |
| | File | Purpose | | |
| |---|---| | |
| | `README.md` | Current SSM LLM status and attention TaoNet comparison | | |
| | `experiments/index.csv` | Searchable run ledger | | |
| | `experiments/runs/<run_id>/manifest.yaml` | Run purpose, commits, data, status | | |
| | `experiments/runs/<run_id>/summary.md` | Human-readable result | | |
| | `experiments/runs/<run_id>/metrics.csv` | Compact metric snapshot | | |
| | `experiments/runs/<run_id>/repobridge.config.json` | Exact remote-run config | | |
| | `experiments/resources/tokenizers/` | Small tokenizer configs only | | |
| | `docs/WORKFLOW.md` | How future runs should be recorded | | |
| | `docs/CURRENT_SSM_LLM_ARCHITECTURE.md` | Current TaoNet-SSM layers, equations, matrices, parameters | | |
| Rule: keep this repo compact. Commit summaries/configs/CSV metrics, not raw output trees or checkpoints. | |
| ## SSM Model Repo | |
| Path: | |
| ```text | |
| C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\Taotern_SSM | |
| ``` | |
| Main code locations: | |
| | Area | File or directory | Notes | | |
| |---|---|---| | |
| | DPLR SSM core | `gamma_space_model/modules/s4_ternary_dplr_ssm.py` | Current main SSM core for TaoNet-SSM | | |
| | Gamma S4 core | `gamma_space_model/modules/ssm_gamma_s4.py` | Older Gamma/S4-style core | | |
| | Baseline Gamma core | `gamma_space_model/modules/ssm_gamma.py` | Baseline/reference SSM | | |
| | SSM blocks | `gamma_space_model/modules/block*.py` | Standalone SSM block wrappers | | |
| | TileLang/Triton fallback area | `csrc/tilelang/` | Capability detection and fallback code | | |
| | Selective scan op wrapper | `gamma_space_model/ops/selective_scan_interface.py` | SSM op interface | | |
| | DPLR profiler | `scripts/profile_dplr_frequency_path.py` | Profiles DPLR frequency path | | |
| | TileLang diagnosis | `scripts/diagnose_tilelang_acceleration.py` | Reports real vs fallback acceleration | | |
| | SSM variant benchmark | `scripts/benchmark_ssm_variants.py` | Standalone SSM benchmarks | | |
| | SSM tests | `tests/test_s4_ternary_dplr_ssm.py`, `tests/test_ssm_gamma*.py` | Core correctness tests | | |
| | Historical record | `EXPERIMENT_RECORD.md` | Older narrative record; new LLM records should be mirrored into this experiment ledger | | |
| When improving the SSM model itself, start from: | |
| ```text | |
| gamma_space_model/modules/s4_ternary_dplr_ssm.py | |
| ``` | |
| When working on hardware acceleration, start from: | |
| ```text | |
| csrc/tilelang/ | |
| scripts/profile_dplr_frequency_path.py | |
| scripts/diagnose_tilelang_acceleration.py | |
| ``` | |
| Remote SSM path used by RepoBridge runs: | |
| ```text | |
| /home/student/YouZheng/gamma_ssm_repo | |
| ``` | |
| ## TaoTrain LLM Repo | |
| Path: | |
| ```text | |
| C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\TaoTrain | |
| ``` | |
| Main code locations: | |
| | Area | File or directory | Notes | | |
| |---|---|---| | |
| | Attention TaoNet baseline | `src/taoTrain/models/taonet.py` | Reference model for comparisons | | |
| | SSM TaoNet wrapper | `src/taoTrain/models/taonet_ssm.py` | Replaces attention core with SSM mixer | | |
| | Model config schema | `src/taoTrain/config.py` | SSM flags live here: hidden dim, mixer dim, shift, kernel mode | | |
| | Model registry | `src/taoTrain/models/registry.py` | Architecture registration | | |
| | Token/data utilities | `src/taoTrain/data/` | JSONL and tokenization data paths | | |
| | Tokenizer trainer | `src/taoTrain/tokenizers/trainer.py` | SentencePiece training path | | |
| | Training loop | `src/taoTrain/training/trainer.py` | Full trainer implementation | | |
| | CLI | `src/taoTrain/cli.py` | TaoTrain command entry | | |
| | Real-token benchmark | `scripts/benchmark_taonet_real_tokens.py` | Main attention vs SSM benchmark for TaoData token tasks | | |
| | Synthetic token benchmark | `scripts/benchmark_taonet_token_variants.py` | Previous/increment/random token probes | | |
| | TaoData pilot tokenizer config | `configs/tokenizer_taodata_pilot.yaml` | Generated pilot 8k SentencePiece tokenizer | | |
| | SSM pretrain config | `configs/ssm_pretrain.yaml` | Config path for SSM pretraining experiments | | |
| | SSM wrapper tests | `tests/test_taonet_ssm.py` | Shape and config behavior tests | | |
| Current real-token benchmark entry point: | |
| ```text | |
| scripts/benchmark_taonet_real_tokens.py | |
| ``` | |
| Current SSM wrapper entry point: | |
| ```text | |
| src/taoTrain/models/taonet_ssm.py | |
| ``` | |
| Current attention baseline: | |
| ```text | |
| src/taoTrain/models/taonet.py | |
| ``` | |
| Remote TaoTrain path used by RepoBridge: | |
| ```text | |
| /home/student/YouZheng/repo | |
| ``` | |
| ## TaoData | |
| GitHub: | |
| ```text | |
| https://github.com/lobakkang/TaoData | |
| ``` | |
| Current local status: | |
| ```text | |
| No local TaoData checkout was found under C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase as of 2026-04-30. | |
| ``` | |
| Remote data path used in current benchmarks: | |
| ```text | |
| /home/student/Data/TaoData/pretrain.jsonl.fineweb.jsonl | |
| ``` | |
| Current pilot tokenizer path on remote: | |
| ```text | |
| /home/student/YouZheng/tokenizers/taodata_pilot_8k/tokenizer.model | |
| /home/student/YouZheng/tokenizers/taodata_pilot_8k/tokenizer.vocab | |
| ``` | |
| Tokenizer config snapshot in this ledger: | |
| ```text | |
| experiments/resources/tokenizers/taodata_pilot_8k.yaml | |
| ``` | |
| When TaoData is cloned locally, update this guide with the exact data download/generation scripts and any preprocessing entry points. | |
| ## RepoBridge | |
| Path: | |
| ```text | |
| C:\Users\YouZheng\Documents\LYZ\MyContent\MyComp\RepoBridge | |
| ``` | |
| Important files: | |
| | File or directory | Purpose | | |
| |---|---| | |
| | `repobridge/core.py` | Sync, SSH, SFTP, run, download implementation | | |
| | `repobridge/cli.py` | CLI entry point | | |
| | `repobridge/app.py` | GUI | | |
| | `CODEX_OPERATOR_GUIDE.md` | Codex remote-run guide | | |
| | `PRODUCTION_RUNBOOK.md` | Production checklist | | |
| | old `repobridge.*.config.json` files | Historical configs; new experiment configs should live in this ledger | | |
| Preferred future location for experiment configs: | |
| ```text | |
| Taotern_LLM_Experiments\experiments\runs\<run_id>\repobridge.config.json | |
| ``` | |
| Remote write root: | |
| ```text | |
| /home/student/YouZheng | |
| ``` | |
| Remote output base: | |
| ```text | |
| /home/student/YouZheng/outputs-taotrain | |
| ``` | |
| Important operational note: | |
| Avoid downloading the whole remote output base if it contains many historical runs. Prefer downloading or copying only the specific run folder. | |
| ## Current Best SSM LLM Path | |
| To inspect the current best SSM LLM implementation: | |
| 1. Open TaoTrain wrapper: | |
| ```text | |
| C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\TaoTrain\src\taoTrain\models\taonet_ssm.py | |
| ``` | |
| 2. Follow the DPLR core import into: | |
| ```text | |
| C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\Taotern_SSM\gamma_space_model\modules\s4_ternary_dplr_ssm.py | |
| ``` | |
| 3. Compare against attention TaoNet: | |
| ```text | |
| C:\Users\YouZheng\Documents\LYZ\MyContent\MyLLM\Codebase\Taotern\TaoTrain\src\taoTrain\models\taonet.py | |
| ``` | |
| 4. Reproduce current best benchmark with: | |
| ```text | |
| Taotern_LLM_Experiments\experiments\runs\2026-04-29_spm_b32_500step_mixer_sweep\repobridge.config.json | |
| ``` | |
| 5. Read current conclusion in: | |
| ```text | |
| Taotern_LLM_Experiments\README.md | |
| ``` | |