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[update] readme

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2026-04-10 10:55:24 +08:00
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README.md
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@@ -1582,7 +1582,7 @@ HF_ENDPOINT=https://hf-mirror.com lm_eval --model hf --model_args pretrained="/p
MiniMind 的数据规模远小于表中其他模型，且训练比例偏向中文，因此英文表现不佳，此外默认没有专门针对这类选择题评测格式做对齐微调，所以表现会相对弱，结果仅供娱乐：
| model name | from | params | zh (ceval / cmmlu) | en (arc / piqa / openbookqa / hellaswag / social_iqa) |
| model name | from | params | zh (ceval / cmmlu) | en (arc / piqa / obqa / hellaswag / siqa) |
|---|---|---|---|---|
| minimind-3 | current | 64M | 24.89 / 25.38 | 28.49 / 50.65 / 23.60 / 28.28 / 34.19 |
| minimind-3-moe | current | 198M | 25.48 / 24.32 | 27.74 / 50.71 / 26.20 / 27.43 / 34.03 |
README_en.md
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@@ -1579,7 +1579,7 @@ HF_ENDPOINT=https://hf-mirror.com lm_eval --model hf --model_args pretrained="/p
MiniMind is trained on far less data than the other models listed here, and its training mix is heavily skewed toward Chinese, so its English performance is relatively weak. It is also not specifically aligned to this multiple-choice evaluation format by default, so the results here are only for entertainment:
| model name | from | params | zh (ceval / cmmlu) | en (arc / piqa / openbookqa / hellaswag / social_iqa) |
| model name | from | params | zh (ceval / cmmlu) | en (arc / piqa / obqa / hellaswag / siqa) |
|---|---|---|---|---|
| minimind-3 | current | 64M | 24.89 / 25.38 | 28.49 / 50.65 / 23.60 / 28.28 / 34.19 |
| minimind-3-moe | current | 198M | 25.48 / 24.32 | 27.74 / 50.71 / 26.20 / 27.43 / 34.03 |