Translation
Transformers
ONNX
Safetensors
Transformers.js
t5
text2text-generation
seq2seq
browser-ml
timers
synthetic-data
text-generation-inference
Instructions to use Satansdeer/timmy-t2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Satansdeer/timmy-t2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="Satansdeer/timmy-t2")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Satansdeer/timmy-t2") model = AutoModelForSeq2SeqLM.from_pretrained("Satansdeer/timmy-t2") - Transformers.js
How to use Satansdeer/timmy-t2 with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('translation', 'Satansdeer/timmy-t2'); - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| pipeline_tag: translation | |
| tags: | |
| - t5 | |
| - seq2seq | |
| - onnx | |
| - transformers.js | |
| - browser-ml | |
| - timers | |
| - synthetic-data | |
| datasets: | |
| - Satansdeer/timmy-t2-timer-sft | |
| license: apache-2.0 | |
| # Timmy T2 | |
| Timmy T2 stands for **Timmy Timer Translator**. It is a tiny, browser-first seq2seq model for translating natural-language timer requests into Timey's compact action DSL. | |
| This is not a new foundation architecture. It is a task-specific fine-tuned T5-style encoder-decoder model plus a compact output language, lossless slot-annotated input format, constrained parser, and browser ONNX runtime package. | |
| ## Release | |
| - Version: `v0.1.0` | |
| - Runtime model version: `phase4y-actions-browser-exact-checkpoint-50-dynq8enc-q4dec-ort-beam4` | |
| - Production commit: `6ea2d2a` | |
| - Production deploy: `6a0ed36e0172c100ef1ab8ac` | |
| - Dataset: [Satansdeer/timmy-t2-timer-sft](https://huggingface.co/datasets/Satansdeer/timmy-t2-timer-sft) | |
| ## Intended Use | |
| Timmy T2 is intended for Timey-style timer planning: | |
| ```text | |
| 5 one minute timers and one 30 second | |
| ``` | |
| The model emits action commands over extracted slot ids: | |
| ```text | |
| REP C0 A0 | |
| ADD A1 | |
| END | |
| ``` | |
| The application parses those commands into concrete timers deterministically. | |
| ## Files | |
| - Root files are the fp32/safetensors checkpoint for Python Transformers. | |
| - `browser/` contains the production browser artifact: | |
| - dynamic q8 encoder ONNX | |
| - q4 decoder ONNX | |
| - tokenizer/config files used by the Timey browser runtime | |
| - `eval/` contains release evaluation summaries. | |
| - `release_manifest.json` records hashes, sizes, evals, and production smoke checks. | |
| ## Training Data | |
| Public dataset rows: | |
| | Split | Rows | | |
| | --- | ---: | | |
| | train | 2639 | | |
| | validation | 207 | | |
| | hard_validation | 62 | | |
| | all_public | 2846 | | |
| The 16-row hidden validation split is withheld from the public dataset to preserve a private holdout. | |
| ## Evaluation | |
| | Eval | Records | Parseable | Strict exact | Semantic exact | Semantic invalid | | |
| | --- | ---: | ---: | ---: | ---: | ---: | | |
| | onnx-dynq8enc-q4dec-validation | 207 | 100% | 100% | 100% | 0% | | |
| | onnx-dynq8enc-q4dec-hard | 62 | 100% | 100% | 100% | 0% | | |
| | onnx-dynq8enc-q4dec-hidden | 16 | 100% | 100% | 100% | 0% | | |
| | onnx-dynq8enc-q4dec-browser-failures | 3 | 100% | 100% | 100% | 0% | | |
| | fp32-validation | 207 | 100% | 100% | 100% | 0% | | |
| | fp32-hard | 62 | 100% | 100% | 100% | 0% | | |
| | fp32-hidden | 16 | 100% | 100% | 100% | 0% | | |
| ## Browser Smoke | |
| The deployed production browser runtime was smoke-tested with service workers enabled. It loaded `timey-t5-efficient-tiny` and produced the expected timer sequences for: | |
| - `5 one minute timers and one 30 second` -> `[60, 60, 60, 60, 60, 30]` | |
| - `first and last timer 5 minute, 5 one minute timers in between` -> `[300, 60, 60, 60, 60, 60, 300]` | |
| ## Limitations | |
| - This is a narrow task model for timer requests, not a general assistant. | |
| - It expects Timey's lossless slot-annotated input format at inference time. | |
| - Correction/edit requests are intentionally handled by deterministic fallback logic in the app. | |
| - Public validation is synthetic and task-targeted; broader natural user traffic should be evaluated before expanding claims. | |