Feature Extraction
Transformers
Safetensors
hetero_moe_projspace
mixture-of-experts
heterogeneous
dimension-projection-space
ffn-fusion
custom_code
Instructions to use redmingwei/HeteroMoEProjSpace-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use redmingwei/HeteroMoEProjSpace-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="redmingwei/HeteroMoEProjSpace-v2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("redmingwei/HeteroMoEProjSpace-v2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
v62.2 → v62.3 的过渡说明
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checkpoints/v62.2/README.md
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# v62.2 —
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| `heteromoe_projspace_v2.pt` | `proj_space`(2.76M)+ `fusion`(50.34M)的权重,**不含**三个骨干 |
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| `report.json` | 训练报告(1500 步,按类别分层 80/20) |
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```
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held-out acc 0.9101 (随机 0.25)
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槽门控 [1.0, 1.0, 1.0, 1.0] 熵 0.6928 均衡 1.000
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专家负载 [0.360, 0.731, 0.538, 0.371] 熵 0.476 均衡 0.492
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```
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# v62 / v62.1 / v62.2 — 均已被 v62.3 取代
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这几个版本的 checkpoint 与报告已移除,原因分两层:
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**一、v62.1 / v62.2 的槽门控结论无效。**
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它们的 `proj_space.router` 是 lazy 创建的(`__init__` 里 `s.router=None`,首次 forward 才建),
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后果三连:
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① `load_state_dict` 时 `proj_space.router.weight` 被判为 unexpected,权重根本加载不上;
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② `trainable` / optimizer 在 router 存在之前就取好,**router 从未被优化**;
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③ 熵正则的梯度流向 router,却不在 optimizer 里,被丢弃。
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⇒ 槽门控完全由一个**随机初始化**的 router 决定。
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「四槽激活率 100%」「槽熵 0.6928」都不成立。
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**专家选择不受影响** —— `fusion.W_router` 是 `nn.Parameter`,从 `__init__` 起就在 `trainable` 里。
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**二、本目录曾被后续版本覆盖。**
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`train_v2.py` 里的 `CKPT_DIR` 曾硬编码为 `checkpoints/v62.2`,于是每次重训都写进同一个目录,
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出现过「目录名是 v62.2、内容却是 v62.3/v62.4」的错位。
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现已改为 `checkpoints/v{VER}`,两者永远一致。
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(实测 v62.4 与 v62.3 的权重**逐位相同**,故内容无损失。)
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## 当前版本
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**[`../v62.3/`](../v62.3/)** —— router 改为 eager 创建,权重能加载、也进 optimizer。
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v62.4 起训练脚本直接 import 仓库里的 `projspace.py`(不再内联副本),训练结果与 v62.3 逐位相同。
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