vinnylarouge/jevlike

选用指南

AI 根据 README 整理 · 2026/10/14 13:56:06

使用方式

通过命令行工具使用，包括数据生成、训练、评估和预测。

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`label` is the zero-based index of the correct option. Each row may have a different number of options, with a minimum of two. ## Quickstart Run these commands from the repository root. They create local synthetic data, train on it, evaluate the saved model and score one new menu. ```sh uv venv source .venv/bin/activate uv pip install -e '.[dev]' jevlike-data synthetic --output data/synthetic jevlike-train data/synthetic/train.jsonl \ --validation data/synthetic/validation.jsonl \ --output runs/synthetic.pt jevlike-eval runs/synthetic.pt data/synthetic/test.jsonl

jevlike-predict runs/synthetic.pt \ --context "Choose the exact badge amber badger. Badge: amber badger." \ --option "azure crane" \ --option "amber badger" \ --option "gold heron" ``` The evaluation prints top-1 accuracy, which is the fraction of correct first choices. Top-3 accuracy is the fraction with the right answer among the three highest scores. Expected calibration error compares confidence with observed accuracy. The command also prints a shuffled-context control, which pairs each menu with the wrong context. A useful model should beat that control. ## Use your own data

部署要求

需要 Python 环境，支持 CPU、Apple MPS 和 NVIDIA CUDA。可选依赖包括游戏环境和 Hugging Face 模型。

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sampled games against a random mover, but 0 wins, 2 draws and 48 losses against Stockfish level 0. The windows were selected for activity and are not typical-play or competence claims. <video src="docs/jevre-demo-10s-bgm.mp4" controls width="960"></video> Install the game extras and record a fresh 640 by 480 Doom trace from the released joint checkpoint: ```sh uv pip install -e '.[games]' python examples/doom/play.py examples/checkpoints/joint-imitation.pt --episodes 10 --game-seconds 35.3 --device cpu --capture-resolution 640x480 --output runs/doom.mp4 --trace runs/doom-trace.json ```

The default byte encoder truncates context to 192 bytes and each option to 32 bytes. Raise `--context-tokens` or `--option-tokens` when your text needs more room. Training supports CPU, Apple MPS for a Mac GPU, and CUDA for an NVIDIA GPU through `--device`. ## Use a frozen pretrained encoder Install the optional dependency and name any compatible encoder from Hugging Face: ```sh uv pip install -e '.[transformers]' jevlike-train data/synthetic/train.jsonl \ --validation data/synthetic/validation.jsonl \ --output runs/qwen-head.pt \ --encoder hf \ --hf-model Qwen/Qwen2.5-0.5B \

已知限制

这是一个研究起点，不是 Jev 的复制品；准确性依赖数据质量；字节编码器对语言理解较弱；预训练路径可能下载大模型并需要更多内存；一次前向传播需要完整的选项列表。

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These numbers describe local experiments, not this quickstart run. We did not show equal quality with Jev or reproduce TypeSafe's private training method. ## Limitations - This is a research starter, not a copy of Jev. - Accuracy depends on data quality, split quality and the encoder. - The byte encoder is cheap but weak on language meaning. - The pretrained path may download a large model and needs more memory. - One-pass scoring requires the complete option list before prediction. - The speed comparison used a small local decoder rather than a large commercial model. ## Licence