README.md

---
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- pretrained
- from-scratch
- tiny-llm-ablation
- custom_code
- tensorboard
- ul2
- moe
- encoder-decoder
datasets:
- HuggingFaceFW/fineweb-edu
model-index:
- name: t5-moe-55M-base
  results:
  - task:
      type: text-generation
      name: Zero-shot continuation likelihood
    dataset:
      type: Rowan/hellaswag
      name: HellaSwag
      config: default
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      name: acc_norm (fraction; lm-eval 0.4.12)
      value: 0.2779326827325234
      args:
        ci95_low: 0.2692569969516344
        ci95_high: 0.28677820246076313
        ci_method: Wilson
  - task:
      type: text-generation
      name: Zero-shot continuation likelihood
    dataset:
      type: allenai/ai2_arc
      name: ARC-Easy
      config: ARC-Easy
      split: test
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      name: acc_norm (fraction; lm-eval 0.4.12)
      value: 0.38930976430976433
      args:
        ci95_low: 0.3698977221380954
        ci95_high: 0.40907915127907174
        ci_method: Wilson
  - task:
      type: text-generation
      name: Zero-shot continuation likelihood
    dataset:
      type: allenai/ai2_arc
      name: ARC-Challenge
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      name: acc_norm (fraction; lm-eval 0.4.12)
      value: 0.2380546075085324
      args:
        ci95_low: 0.21455235182352161
        ci95_high: 0.26326840760453163
        ci_method: Wilson
  - task:
      type: text-generation
      name: Zero-shot continuation likelihood
    dataset:
      type: baber/piqa
      name: PIQA
      config: default
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      name: acc_norm (fraction; lm-eval 0.4.12)
      value: 0.5560391730141458
      args:
        ci95_low: 0.5332313404997734
        ci95_high: 0.5786132479763156
        ci_method: Wilson
  - task:
      type: text-generation
      name: Zero-shot continuation likelihood
    dataset:
      type: allenai/winogrande
      name: WinoGrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: acc
      name: acc (fraction; lm-eval 0.4.12)
      value: 0.4846093133385951
      args:
        ci95_low: 0.4571789703267686
        ci95_high: 0.512132701298903
        ci_method: Wilson
  - task:
      type: text-generation
      name: Zero-shot continuation likelihood
    dataset:
      type: allenai/openbookqa
      name: OpenBookQA
      config: main
      split: test
      args:
        num_few_shot: 0
    metrics:
    - type: acc_norm
      name: acc_norm (fraction; lm-eval 0.4.12)
      value: 0.262
      args:
        ci95_low: 0.22537626941723765
        ci95_high: 0.30225291664246134
        ci_method: Wilson
  - task:
      type: text-generation
      name: Zero-shot continuation likelihood
    dataset:
      type: aps/super_glue
      name: BoolQ
      config: boolq
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: acc
      name: acc (fraction; lm-eval 0.4.12)
      value: 0.499388379204893
      args:
        ci95_low: 0.48226179513749795
        ci95_high: 0.5165163985990305
        ci_method: Wilson
  - task:
      type: text-generation
      name: Zero-shot continuation likelihood
    dataset:
      type: EleutherAI/lambada_openai
      name: LAMBADA OpenAI
      config: default
      split: test
      args:
        num_few_shot: 0
    metrics:
    - type: acc
      name: acc (fraction; lm-eval 0.4.12)
      value: 0.14826314768096255
      args:
        ci95_low: 0.13882265856941103
        ci95_high: 0.1582276717641896
        ci_method: Wilson
  - task:
      type: text-generation
      name: Continuation likelihood
    dataset:
      type: AxiomicLabs/Arithmark-3.0
      name: ArithMark-3
      config: default
      split: train
    metrics:
    - type: acc_norm
      name: acc_norm (fraction; lm-eval 0.4.12 comparison protocol)
      value: 0.342
      args:
        dtype: bfloat16
        num_few_shot: 0
        max_length: 1024
        standard_error: 0.015008706182121804
        evaluation_date: '2026-10-01'
        ci95_low: 0.3132528988050859
        ci95_high: 0.37195635687634954
        ci_method: Wilson 95%; item independence approximation
  - task:
      type: text-generation
      name: Continuation likelihood
    dataset:
      type: pkavumba/balanced-copa
      name: Balanced COPA
      config: default
      split: train
    metrics:
    - type: acc
      name: acc (fraction; lm-eval 0.4.12 comparison protocol)
      value: 0.538
      args:
        dtype: bfloat16
        num_few_shot: 0
        max_length: 2048
        standard_error: 0.015773547629015002
        evaluation_date: '2026-10-01'
        ci95_low: 0.5070132970793998
        ci95_high: 0.5686958692756982
        ci_method: Wilson 95%; item independence approximation
  - task:
      type: text-generation
      name: Continuation likelihood
    dataset:
      type: tau/commonsense_qa
      name: CommonsenseQA
      config: default
      split: validation
    metrics:
    - type: acc
      name: acc (fraction; lm-eval 0.4.12 comparison protocol)
      value: 0.20065520065520065
      args:
        dtype: bfloat16
        num_few_shot: 0
        max_length: 2048
        standard_error: 0.011466011466011467
        evaluation_date: '2026-10-01'
        ci95_low: 0.17914588148536167
        ci95_high: 0.2240421844184298
        ci_method: Wilson 95%; item independence approximation
  - task:
      type: text-generation
      name: Continuation likelihood
    dataset:
      type: allenai/sciq
      name: SciQ (with support)
      config: default
      split: test
    metrics:
    - type: acc_norm
      name: acc_norm (fraction; lm-eval 0.4.12 comparison protocol)
      value: 0.654
      args:
        dtype: bfloat16
        num_few_shot: 0
        max_length: 2048
        standard_error: 0.01505026612756434
        evaluation_date: '2026-10-01'
        ci95_low: 0.6239780184885133
        ci95_high: 0.6828433398979359
        ci_method: Wilson 95%; item independence approximation
  - task:
      type: text-generation
      name: Continuation likelihood
    dataset:
      type: truthfulqa/truthful_qa
      name: TruthfulQA MC2
      config: multiple_choice
      split: validation
    metrics:
    - type: acc
      name: acc (fraction; lm-eval 0.4.12 comparison protocol)
      value: 0.4656644987453161
      args:
        dtype: bfloat16
        num_few_shot: 0
        max_length: 2048
        standard_error: 0.016000577772697092
        evaluation_date: '2026-10-01'
  - task:
      type: text-generation
      name: Continuation likelihood
    dataset:
      type: BananaMind/BananaMind-Base-Bench-1.1
      name: BananaMind Base 1.1
      config: default
      split: test
    metrics:
    - type: raw_accuracy
      name: raw_accuracy (fraction; lm-eval 0.4.12 comparison protocol)
      value: 0.36
      args:
        dtype: bfloat16
        num_few_shot: 0
        max_length: 2048
        standard_error: 0.025693810923465083
        evaluation_date: '2026-10-01'
        ci95_low: 0.3114835744745811
        ci95_high: 0.41155622892601307
        ci_method: Wilson 95%; item independence approximation
  - task:
      type: text-generation
      name: Continuation likelihood
    dataset:
      type: cais/mmlu
      name: MMLU continuation
      config: 57 subjects
      split: test
    metrics:
    - type: acc
      name: acc (fraction; lm-eval 0.4.12 comparison protocol)
      value: 0.24932345819683804
      args:
        dtype: bfloat16
        num_few_shot: 0
        max_length: 2048
        standard_error: 0.003640204268569072
        evaluation_date: '2026-10-01'
  - task:
      type: text-generation
      name: Continuation likelihood
    dataset:
      type: nyu-mll/blimp
      name: BLiMP
      config: 67 minimal-pair subsets
      split: train
    metrics:
    - type: acc
      name: acc (fraction; lm-eval 0.4.12 comparison protocol)
      value: 0.6988955223880597
      args:
        dtype: bfloat16
        num_few_shot: 0
        max_length: 2048
        standard_error: 0.0015386258071003309
        evaluation_date: '2026-10-01'
---

# T5 MoE 55M Base (UL2)

A **54,858,240-parameter** English base model in the **Tiny llm ablation** experiment. Trained from scratch on exactly **3,932,160,000 source tokens** over **15,000 optimizer steps**. The token count measures processed input blocks, not unique text or supervised target tokens.

## Architecture and references

6 encoder + 6 decoder layers, width 512; encoder 8-head attention, decoder 8 query / 2 KV heads; 8 experts per MoE layer, top-2 routing, expert width 160; RoPE, RMSNorm, FP32 residuals and tied shared embeddings. Maximum encoder length 2050 including controls; raw training blocks 2048.

Architecture inspiration: [yandex/AliceAI-T5-35B-A0.6B](https://huggingface.co/yandex/AliceAI-T5-35B-A0.6B). Tokenizer foundation: [q-project/Q-50M-Base](https://huggingface.co/q-project/Q-50M-Base), preserving all 32,768 original IDs and adding 3 mode tokens + 512 sentinels (33,283 entries). Weights were initialized randomly. This small adaptation does not reproduce Alice’s corpus, optimizer or routing recipe.

## Training

- Data: [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), `sample-10BT`, streamed from local Parquet shards; shuffle buffer 100,000.
- Objective: UL2 with seven equally likely denoisers: R(15%, mean span 3/8), S(suffix), X(50%,3), X(50%,8), X(15%,64), X(50%,64). S masks a uniformly sampled suffix of length 1..L/2. Targets contain corrupted spans and control tokens. Training adds router auxiliary loss with coefficient 0.01. These sampler choices are explicit local choices; see [UL2](https://arxiv.org/abs/2205.05131).
- Batch: 16 sequences × 8 accumulation × 2048 tokens = 262,144 source tokens per step.
- Fused AdamW; peak LR 0.001, betas (0.9, 0.95), weight decay 0.1 (no decay for bias/norm/1D parameters), gradient clipping 1.0. Linear warmup for 150 steps, then cosine decay to 10% of peak LR.
- BF16 compute on one RTX 5070 Ti (16 GB), seed 2026; checkpoints retain FP32 weights. [Exact training configuration](training_config.json).

## Evaluation

Full selected task splits, **no added few-shot examples**, **lm-eval 0.4.12**, no chat template, BF16 on RTX 5070 Ti, maximum context 2048 (ArithMark: 1024). Accuracy is a percentage. **± is one standard error; the separate bracketed column is the 95% Wilson confidence interval.** Intervals describe finite evaluation-sample uncertainty, not variation across training seeds; no multiple-comparison correction is applied.

| Dataset | Split | Examples | Metric | Score ± SE (%) | 95% CI (%) |
|---|---|---:|---|---:|---:|
| [HellaSwag](https://huggingface.co/datasets/Rowan/hellaswag) | validation | 10,042 | `acc_norm` | 27.79 ± 0.45 | [26.93, 28.68] |
| [ARC-Easy](https://huggingface.co/datasets/allenai/ai2_arc) | test | 2,376 | `acc_norm` | 38.93 ± 1.00 | [36.99, 40.91] |
| [ARC-Challenge](https://huggingface.co/datasets/allenai/ai2_arc) | test | 1,172 | `acc_norm` | 23.81 ± 1.24 | [21.46, 26.33] |
| [PIQA](https://huggingface.co/datasets/baber/piqa) | validation | 1,838 | `acc_norm` | 55.60 ± 1.16 | [53.32, 57.86] |
| [WinoGrande](https://huggingface.co/datasets/allenai/winogrande) | validation | 1,267 | `acc` | 48.46 ± 1.40 | [45.72, 51.21] |
| [OpenBookQA](https://huggingface.co/datasets/allenai/openbookqa) | test | 500 | `acc_norm` | 26.20 ± 1.97 | [22.54, 30.23] |
| [BoolQ](https://huggingface.co/datasets/aps/super_glue) | validation | 3,270 | `acc` | 49.94 ± 0.87 | [48.23, 51.65] |
| [LAMBADA OpenAI](https://huggingface.co/datasets/EleutherAI/lambada_openai) | test | 5,153 | `acc` | 14.83 ± 0.50 | [13.88, 15.82] |
| [ArithMark-3](https://huggingface.co/datasets/AxiomicLabs/Arithmark-3.0) | train | 1,000 | `acc_norm` | 34.20 ± 1.50 | [31.33, 37.20] |
| [Balanced COPA](https://huggingface.co/datasets/pkavumba/balanced-copa) | train | 1,000 | `acc` | 53.80 ± 1.58 | [50.70, 56.87] |
| [CommonsenseQA](https://huggingface.co/datasets/tau/commonsense_qa) | validation | 1,221 | `acc` | 20.07 ± 1.15 | [17.91, 22.40] |
| [SciQ (with support)](https://huggingface.co/datasets/allenai/sciq) | test | 1,000 | `acc_norm` | 65.40 ± 1.51 | [62.40, 68.28] |
| [TruthfulQA MC2](https://huggingface.co/datasets/truthfulqa/truthful_qa) | validation | 817 | `acc` | 46.57 ± 1.60 | — |
| [BananaMind Base 1.1](https://huggingface.co/datasets/BananaMind/BananaMind-Base-Bench-1.1) | test | 350 | `raw_accuracy` | 36.00 ± 2.57 | [31.15, 41.16] |
| [MMLU continuation](https://huggingface.co/datasets/cais/mmlu) | test | 14,042 | `acc` | 24.93 ± 0.36 | — |
| [BLiMP](https://huggingface.co/datasets/nyu-mll/blimp) | train | 67,000 | `acc` | 69.89 ± 0.15 | — |

T5 uses UL2 S-mode: encoder S + prefix + sentinel + EOS; decoder BOS + sentinel + shifted answer. Only answer text is scored; control tokens and router loss are excluded, with the full vocabulary retained in the softmax. Its encoder sees at most 2047 text-prefix tokens after reserving controls. LAMBADA accuracy requires the complete final-word token sequence. `acc_norm` is harness length-normalized option scoring; raw accuracy is also stored in [results.json](evaluation/results.json).

**[WikiText-2 raw test](https://huggingface.co/datasets/Salesforce/wikitext), conditional continuation:** CPU FP32 re-evaluation on 291 nonoverlapping blocks (512 prefix + 512 scored suffix tokens), 148,992 scored tokens; 335 tail tokens excluded. NLL **3.920357**, 95% CI **[3.879299, 3.960742]**; token PPL **50.418**, 95% CI **[48.390, 52.496]**. Percentile block bootstrap, 10,000 resamples, seed 2026; exponentiate NLL endpoints for PPL. Blocks are the resampling unit; this does not model all within-document dependence. This is not standard rolling AR or word PPL. The earlier BF16 point is retained separately in TensorBoard, with no borrowed FP32 interval.

The metadata contains author-reported `model-index` scores. The evaluated dataset repositories had no registered `eval.yaml` on 2026-09-20, so no `.eval_results` leaderboard entry or verified badge is claimed. [Machine-readable results and provenance](evaluation/results.json).

Full selected splits; lm-eval 0.4.12; seed 1234; BF16 on RTX 5070 Ti;
context cap 2048 (ArithMark 1024), TF32 disabled, no chat template and no added
few-shot examples. TruthfulQA retains the harness's fixed six-QA preamble.
ArithMark and BananaMind normalize by continuation token count; ordinary harness
acc_norm uses its own length normalization. BananaMind is raw accuracy, not Elo.
SciQ includes the support passage. Balanced COPA uses the mirrored 1000-item
train-named evaluation split; cRia's split was inferred, not confirmed.
MMLU scores full answer continuations across 57 subjects, weighted by item count;
BLiMP averages 67 equal-sized minimal-pair subsets. Standard errors are retained
from each evaluator. Wilson intervals are reported only where the runner logged
binary item accuracy; MC2 is probability mass, not binary accuracy. These
intervals do not model dependence between paired/templated examples or training
seed variation. UL2, PrefixLM and experimental diffusion PLL use their documented
conditional scoring protocols; PLL exposes the other answer tokens and is not
autoregressive likelihood. cRia's published scores used a different precision
and benchmark-adapted checkpoint; this completes our comparison coverage, not
an independent reproduction of cRia or an official leaderboard submission.

[Full results, provenance and group scores](evaluation/comparison-20261001/results.json). [Updated machine-readable results](evaluation/results.json). [TensorBoard events](tensorboard/) contain these new scores at step 15,000.



## Usage

Install `requirements.txt` (tested with Transformers 5.17.0 / PyTorch 2.11.0). Custom model code is included; `trust_remote_code=True` is required. This example runs on CPU.

```python
import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
repo = "d0rj/t5-moe-55M-base"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForSeq2SeqLM.from_pretrained(repo, trust_remote_code=True).eval()
c = model.config.ul2
prefix = tokenizer.encode("The capital of France is", add_special_tokens=False)
inputs = torch.tensor([[c["mode_ids"]["S"], *prefix, c["sentinel_ids"][0], c["eos_id"]]])
decoder = torch.tensor([[model.config.decoder_start_token_id, c["sentinel_ids"][0]]])
output = model.generate(input_ids=inputs, decoder_input_ids=decoder,
                        max_new_tokens=64, do_sample=False)
print(tokenizer.decode(output[0, decoder.shape[1]:], skip_special_tokens=True))
```

To reproduce the core evaluation from a downloaded repository, install `evaluation/requirements.txt` and run:

```bash
python evaluation/run_core.py --device cuda:0 --dtype bfloat16 --batch-size 16 --output evaluation-rerun
```

To reproduce after downloading this model repository, accept the BananaMind dataset terms, authenticate with `hf auth login`, then run in a suitable CUDA environment:

```bash
pip install -r evaluation/comparison-20261001/repro/requirements.txt
python evaluation/comparison-20261001/repro/run.py --device cuda:0 --dtype bfloat16 --batch-size 8 --output comparison-rerun
```

The bundled runner uses the published model classes with the exact evaluation adapters and tokenizer. `--limit` produces smoke results only. Raw dataset examples are not included in this release.

## TensorBoard and limitations

[TensorBoard event files](tensorboard/) include training telemetry and `eval/<task>/<metric>` at step 15,000, plus separate CI bounds. Training telemetry covers steps 20–15,000 (750 loss points), including token CE, router loss, gradient norm, throughput, memory, padding and denoiser fractions.

These are small English continuation models, not instruction-tuned assistants. Equal source-token budgets do not imply equal target-token supervision or FLOPs. Benchmark contamination was not audited; results are from one training seed. Reference-model scores from different prompts, tokenizers or corpora are not directly interchangeable.