Instructions to use tdelab/tde-general-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use tdelab/tde-general-v0.2 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir tde-general-v0.2 tdelab/tde-general-v0.2
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
TDE general v0.2 — the typed-decision encoder, now also playing Snake
The same 150M encoder as tde-general-v0.1 (ModernBERT-base, pointer
readout; one forward pass over state + question + candidates returns a probability per candidate), fine-tuned further
so it plays Snake from a plain-text board while keeping its general decisions.
Snake
The state is the board as text, one token per cell ( . empty, F food, H head, 1– 9 body: moves until that
cell is free) under a line with the length and heading; the question is "Which way should the snake move?" with the
candidates up, down, left and right. The top candidate is the move. An anti-trap shield vetoes a move after which the
head can no longer reach its tail and takes the model's next choice instead.
| laya-mlx demo protocol: 24x16, length 6, seeds 101–104, 600 moves | food per game | survived |
|---|---|---|
| tde-general-v0.2 with the anti-trap shield | 39 · 38 · 40 · 38 (mean 38.75) | 4 of 4 |
| tde-general-v0.2, model alone | 27 · 18 · 13 · 29 (mean 21.75) | 0 of 4 |
| laya-mlx as published (planner features in the options, Hamiltonian-cycle shield) | 20 · 24 · 23 · 16 (mean 20.75) | 4 of 4 |
About 12 ms per move with MLX on an M5 Pro. A live page where the model plays game after game:
python -m integrations.snake.demo in the code repository (tutorial:
integrations/snake).
General decisions
| In-distribution test (7 public datasets, 2,000-item sample) | accuracy | NLL |
|---|---|---|
| tde-general-v0.1 | 87.4% | 0.313 |
| tde-general-v0.2 | 86.4% | 0.336 |
JevBench and the typed-decisions test were not re-run for this checkpoint.
Training
Initialised from tde-general-v0.1 and fine-tuned for 2 epochs (6,250 steps of 32) on 100k Snake positions from 8x8,
12x12 and 24x16 boards, each labelled with the moves of a shortest-path planner that keeps its tail reachable. Trained
with MLX on one Mac in about 80 minutes (integrations/snake/build_sft.py, then tde/mlx/train.py).
Usage
# pip install git+https://github.com/e13ven-arch/tde (package name: tde)
from tde.inference import Decider
d = Decider.from_run("tdelab/tde-general-v0.2") # or a local release folder
d.decide(board_text, {"type": "choice", "instructions": "Which way should the snake move?",
"criteria": {m: m for m in ("up", "down", "left", "right")}})
General questions work exactly as with v0.1.
Limitations
Without the shield the snake eventually traps itself: all four protocol games end before move 600. General accuracy is about one point below v0.1; the limitations listed for v0.1 apply unchanged.
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