Mapika/decider

选用指南

AI 根据 README 整理 · 2026/10/16 20:12:00

使用方式

通过 Python 库使用，提供 Decider 类进行决策，也支持 HTTP 服务器和 vLLM 服务。

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*Recorded episodes; every move is one forward pass, and the bars are the served probabilities. Tetris: the harness shortlists 8 placements with a hand-tuned heuristic and states their consequences, and the model picks one (20 lines per game, against 0.6 for a random pick from the same 8). Pong uses an unreleased games-RL overlay. Sources, seeds and windows: [docs/DEMOS.md](https://github.com/Mapika/decider/blob/main/docs/DEMOS.md).* ```bash pip install decider-ai # the import name is decider ``` ```python from decider.infer import Decider

`noul` value is the probability of yes. A Noul question may omit `instructions` if its `criteria` describe true or false. `examples/` has three complete programs: confidence-gated routing, composite scoring, and a hierarchical beam over Choice probabilities. ### HTTP server ```bash scripts/serve.sh Mapika/decider-2b 8000 # listens on 127.0.0.1; DECIDER_HOST=0.0.0.0 opens it to the network (no authentication) ``` `POST /v1/systemone` is TypeSafe's wire format, so their SDKs work unchanged with `TYPESAFE_BASE_URL=http://localhost:8000`;

部署要求

需要 Python 环境，支持 CUDA、MPS 或 CPU；不同模型有不同显存需求，如 2B 约 4GB，4B 8.4GB，35B 65GB（bf16）或 19.6GB（NVFP4）。

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## Runs on * **CUDA.** bf16, `torch.compile`, shape-bucketed CUDA graphs, optional FP8 (e4m3) linears. The 2B needs about 4 GB, the 4B 8.4 GB, the 35B 65 GB in bf16 or 19.6 GB in NVFP4. * **Apple Silicon, MPS.** Acceleration for the dense models (0.8B, 2B, 2B vision), merged 2026-09-22 from pull request #2 by **@simply-sunny**. On an M1 Pro in float16, across the three 2B smoke-test workloads, the median request is 133 ms with the patch and 171 ms without it; on the held-out MASSIVE

已知限制

模型在知识领域（如 GPQA、GSM8K）上落后于 Jev，且硬决策校准较差。

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| retrieval | 0.34 | 0.37 | | tools | 0.72 | 0.73 | | arts | 0.53 | 0.56 | Language, retrieval, tools and arts are within 0.03. Knowledge is 0.18 behind, on GPQA, GSM8K, CRUXEval and MMLU. The same weakness shows on JevBench's 111 public hard items, which are long policy texts, multi-hop and temporal-numeric reasoning:

decider-35b-a3b 0.676, decider-4b v2.1 0.649 and decider-2b v11 0.577 against Jev's 0.730; decider-12b v1 (stock Gemma-4-12B-it) reaches 0.730 and v2 0.712 (our runner and the harness's own per-task file, same items). The other axis we lose there is calibration on hard items; see [Limits](#limits-stated-plainly). ## Models Held-out means no example of that dataset was trained on. The regression set has 28 held-out tasks, the 94-task set 24; the two are not comparable to each other, and the NVFP4 row is measured against the bf16 build rather than on a held-out set.