---
license: mit
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- taonet
- taotern
- ssm
- state-space-model
- dplr
- pytorch
- transformers
- custom_code
- text-generation
- experimental
datasets:
- TaoData
---

# TaoNet-mini-T2

TaoNet-mini-T2 is an experimental 196M-parameter TaoNet language model using a Taotern/Gamma DPLR state-space model (SSM) sequence core instead of attention. The repository includes the full training handoff package, but the recommended inference path is now Hugging Face `transformers` remote code:

```python
AutoModelForCausalLM.from_pretrained("TaoTern/TaoNet-mini-T2", trust_remote_code=True)
```

The default `transformers` loader downloads `model/pretrain_final_model.pt` and applies the RepoBridge chat-quality fix: `ssm_finite_tail_correction=True` and `ssm_kernel_mode="recurrent"`.

## Quick Start

Install runtime dependencies:

```bash
pip install torch transformers sentencepiece huggingface_hub pydantic pydantic-settings pyyaml numpy
```

For the private review repo, log in first:

```bash
hf auth login
```

Run generation from Python:

```python
import time
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

MODEL_NAME = "TaoTern/TaoNet-mini-T2"

device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.bfloat16 if device == "cuda" else torch.float32

tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    MODEL_NAME,
    trust_remote_code=True,
    torch_dtype=dtype,
).to(device)


def generate_text(prompt, max_new_tokens=64, temperature=0.7, top_p=0.85):
    inputs = tokenizer(prompt, return_tensors="pt")
    inputs = {key: value.to(device) for key, value in inputs.items()}

    start_time = time.time()
    with torch.inference_mode():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            temperature=temperature,
            top_p=top_p,
            repetition_penalty=1.2,
            do_sample=True,
            use_cache=False,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )
    elapsed_time = time.time() - start_time

    new_tokens = outputs.shape[1] - inputs["input_ids"].shape[1]
    tokens_per_second = new_tokens / elapsed_time if elapsed_time > 0 else 0.0
    completion = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
    return completion, tokens_per_second


if __name__ == "__main__":
    text, tps = generate_text("Fruit is now expensive so we should")
    print(text)
    print(f"\nTokens per second: {tps:.2f}")
```

To load the SFT final checkpoint instead of the default pretrain checkpoint:

```python
model = AutoModelForCausalLM.from_pretrained(
    "TaoTern/TaoNet-mini-T2",
    trust_remote_code=True,
    checkpoint_name="final_model.pt",
)
```

## Model Details

| Field | Value |
|---|---:|
| Architecture | `taonet_ssm` |
| Candidate | `pure_ssm_196m_branch_rms_only` |
| Parameters | 196,573,128 |
| Hidden dimension | 1024 |
| Layers | 18 |
| FFN dimension | 3072 |
| Sequence length | 512 |
| Tokenizer | TaoData pilot SentencePiece 8k |
| SSM core | DPLR |
| SSM hidden dimension | 32 |
| SSM mixer dimension | 256 |
| SSM lanes | 2 split lanes |
| SSM gate | Channel gate |
| Local shift | Enabled, per-channel |
| Branch RMS norm | Enabled |

## Repository Layout

```text
config.json
configuration_taonet_mini_t2.py
modeling_taonet_mini_t2.py
tokenization_taonet_mini_t2.py
tokenizer.model
model/
  final_model.pt                  # SFT final checkpoint
  pretrain_final_model.pt         # default checkpoint for HF inference
tokenizer/
  tokenizer.model
  tokenizer.vocab
code/
  TaoTrain/
  Taotern_SSM/
  Taotern_LLM_Experiments/
artifacts/
  configs/
  diagnostics/
chat_ssm_fixed.py                 # legacy local fixed-chat CLI
eval_lm_eval.py                   # local lm-eval harness wrapper
```

## Upload Notes

This repo contains two multi-GB checkpoint files, so prefer the resumable large-folder uploader instead of the normal single-commit upload command:

```bash
hf upload-large-folder TaoTern/TaoNet-mini-T2 . --repo-type model --private
```

On Windows, from the repo folder:

```powershell
powershell -ExecutionPolicy Bypass -File .\upload_large_folder.ps1
```

## Inference Notes

The training config used `ssm_finite_tail_correction=False` and `ssm_kernel_mode="conv"`. That path is fast for full-sequence training/evaluation but produced poor chat samples in the recovered workflow.

The `transformers` wrapper defaults to:

```text
ssm_finite_tail_correction=True
ssm_kernel_mode=recurrent
checkpoint=model/pretrain_final_model.pt
```

For fast benchmark scoring, use the included `eval_lm_eval.py` script with `--ssm-kernel-mode conv --finite-tail`.

## LM Evaluation Harness Benchmark

Settings:

```text
library=lm-eval-harness
checkpoint=model/pretrain_final_model.pt
num_fewshot=0
limit=100
ssm_kernel_mode=conv
ssm_finite_tail_correction=true
eval_batch_size=8
```

Results:

| Task | Primary score |
|---|---:|
| HellaSwag | 0.3300 |
| ARC Easy | 0.3400 |
| ARC Challenge | 0.2200 |
| PIQA | 0.4400 |
| Winogrande | 0.5300 |
| Mean primary score | 0.3720 |

These are limit-100 smoke benchmark numbers for review, not full leaderboard results.

## Training Summary

Run ID:

```text
taotern-200m-branch-only-chat-20260514
```

| Stage | Value |
|---|---:|
| Pretrain token positions | 4,000,000,000 |
| Pretrain steps | 976,563 |
| SFT steps | 50,000 |
| Batch size | 8 |
| Sequence length | 512 |
| Pretrain LR | 8e-4 |
| SFT LR | 5e-5 |

Compact post-run statistics:

| Metric | Value |
|---|---:|
| Pretrain first loss | 9.26 |
| Pretrain last loss | 2.64 |
| Pretrain tail-100 mean | 2.3351 |
| SFT first loss | 3.20 |
| SFT last loss | 1.08 |
| SFT tail-100 mean | 0.9585 |
| Activation probe loss | 2.8460 |
| Final block RMS | 45.97 |
| Final block max abs | 2560.03 |

## Intended Use

This model is intended for:

- Taotern/TaoNet SSM research
- checkpoint backup and reproducibility
- deployment experiments with custom Hugging Face remote code
- studying recurrent SSM inference behavior

## Limitations

- Experimental model quality; validate outputs before use.
- Requires `trust_remote_code=True` because the architecture is not part of upstream `transformers`.
- The recommended chat path depends on an inference-time SSM override.
- CPU inference is expected to be very slow.
- English-focused pilot data/tokenizer.

## Citation

```bibtex
@software{taonet_mini_t2_2026,
  title = {TaoNet-mini-T2: TaoNet SSM Language Model Checkpoint},
  author = {TaoTern},
  year = {2026},
  url = {https://huggingface.co/TaoTern/TaoNet-mini-T2}
}
```

## Related

- [TaoTern/TaoNet-pico-T1](https://huggingface.co/TaoTern/TaoNet-pico-T1)