| --- |
| license: apache-2.0 |
| base_model: internlm/Intern-Decision-0.8B |
| library_name: coreml |
| pipeline_tag: text-classification |
| tags: |
| - coreml |
| - apple-silicon |
| - decision |
| - qwen3.5 |
| - pokemon |
| - pokemon-showdown |
| --- |
| |
| # Intern-Decision-0.8B, fine-tuned for Pokémon Showdown, for Core ML |
|
|
| [internlm/Intern-Decision-0.8B](https://huggingface.co/internlm/Intern-Decision-0.8B) (Shanghai AI Laboratory, |
| Apache-2.0) fine-tuned to choose Pokémon Showdown battle actions, exported for Core ML. The stock model plays at chance; |
| this one learned from its own 4B sibling, [internlm/Intern-Decision-4B](https://huggingface.co/internlm/Intern-Decision-4B), |
| over one night on a MacBook Pro. Inspired by SGLang's Qwen3.8-27B FireRed run; this is the small, local end of that |
| idea, on the Showdown simulator rather than the game. |
|
|
| ## How it was trained |
|
|
| - **Teacher.** Intern-Decision-4B played 220 `gen9randombattle` battles on a local Showdown server through |
| [poke-env](https://github.com/hsahovic/poke-env) against poke-env's random, max-base-power and simple-heuristics |
| players and itself; every turn's request and probability vector was logged (6,974 decisions). |
| - **Request.** A compact JSON state (both teams, HP, status, boosts, field) and one `choice` question whose options are |
| the legal moves and switches with their facts (type, category, power, STAB, effectiveness, accuracy, PP; switch |
| matchups). Exactly Intern-Decision's own wire format, rendered by the checkpoint's `inference.py`. |
| - **Student.** LoRA r=32 on every language-model linear layer, 1.5 epochs, KL(teacher ‖ student) on the |
| temperature-scaled restricted softmax at the `<decision>` marker, option order shuffled per sample. 200 minutes on |
| an M5 Pro (PyTorch MPS). Merged into the weights before export. Held-out agreement with the 4B's top choice: 35% → 80%. |
| - **Not used.** The 27B, any GPU server, or any game ROM. |
|
|
| ## Results (gen9 random battles, this model's side first) |
|
|
| | Player | vs random | vs max-base-power | vs simple heuristics | |
| | --- | ---: | ---: | ---: | |
| | stock Intern-Decision-0.8B (Core ML), 10 battles | 2-3 | 1-9 | 1-9 | |
| | Intern-Decision-4B teacher (PyTorch), 60 battles | 58-2 | 45-15 | 16-44 | |
| | **this model** (Core ML), 30 battles | 9-1 (10 played) | 24-6 | 9-21 | |
|
|
| It matches its teacher and loses to poke-env's heuristic bot most of the time, like the teacher does. Latency on an |
| M5 Pro GPU: 89 ms per decision in the 512-token bucket, 125 ms in the 640, 180 ms in the 1024 (typical battle |
| requests are 430–560 tokens). |
|
|
| ## Files |
|
|
| | Path | What | |
| | --- | --- | |
| | `L512_F8/`, `L640_F8/`, `L1024_F16/` | `DecisionRow_w8.mlpackage` (int8 weights, 480 MB) and `DecisionRow_fp16.mlpackage` (955 MB) + `config.json` per bucket (tokens × fields) | |
| | `multi/DecisionRow_w8.mlpackage` | all three buckets as one weight-shared multifunction package (488 MB, functions `L512_F8` / `L640_F8` / `L1024_F16`; needs macOS 15 / iOS 18 to select a function) | |
| | `embeddings.f16` | token embeddings (fp16, 248,320 × 1,024), gathered on the host | |
| | `tokenizer.json` | the checkpoint's tokenizer (Qwen3.5 + `<decision>`) | |
|
|
| int8 (per-channel, weight-only) is the recommended download: measured on the Swift runtime with one bucket in use it |
| is about 0.85 GB in memory (374 MB process footprint + 480 MB mapped weights) against about 1.5 GB for fp16, at the |
| same 90 ms per decision, and it played 15-0 against poke-env's max-base-power player where fp16 played 14-1 (15 |
| battles each). FluidUse's `.showdown` snapshot uses the int8 buckets. |
|
|
| Same inputs and outputs as [FluidInference/intern-decision-0.8b-coreml](https://huggingface.co/FluidInference/intern-decision-0.8b-coreml): |
| `hidden` [1, L, 1024], `cos` / `sin` [L, 64], `field_onehot` [F, L] → `logits` [F, 62] over the answer symbols; softmax |
| over the first *n* symbols, log-probabilities divided by the temperature. It is a general typed-decision model that |
| happens to know Pokémon: any state and question set works, but the fine-tune was only evaluated on battles. Runtime: |
| `InternDecisionManager` in [FluidUse](https://github.com/FluidInference/FluidUse); harness, training and export code in |
| [mobius](https://github.com/FluidInference/mobius) under `models/computer-use/intern-decision-0.8b/showdown/`. |
|
|
