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
| # Experiments | |
| This directory stores compact experiment records. It should make it possible for another Codex thread or a human researcher to answer: | |
| - What was tested? | |
| - Which code commits were used? | |
| - Which exact remote command/config ran? | |
| - What were the key metrics? | |
| - What did we learn? | |
| - What should be tried next? | |
| ## Artifact Rules | |
| Commit: | |
| - `manifest.yaml` | |
| - `summary.md` | |
| - `metrics.csv` | |
| - exact `repobridge.config.json` | |
| - small tokenizer/training configs | |
| Do not commit: | |
| - raw remote output trees | |
| - model checkpoints | |
| - tokenizer binaries | |
| - huge logs | |
| - duplicated historical output folders | |
| Raw outputs may remain in TaoTrain local `results/` or on the remote server. This repo keeps the distilled evidence. | |
| ## Legacy Configs | |
| `legacy_repobridge_configs/` contains exact copies of old RepoBridge root experiment configs. These are intentionally separate from curated run folders: | |
| - curated runs: interpreted, summarized, and often have metrics | |
| - legacy configs: exact historical command/config snapshots that have not all been fully interpreted yet | |
| ## Run Folder Contract | |
| Each run folder should contain: | |
| ```text | |
| manifest.yaml | |
| summary.md | |
| metrics.csv | |
| repobridge.config.json | |
| ``` | |
| If a run failed before producing metrics, keep `manifest.yaml`, `summary.md`, and the config, and mark `status: interrupted` or `status: failed`. | |