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model : add BailingMoeV2 support (#16063)
* add BailingMoeV2 support * update llm types * undo * undo * update llm types * add model collection link * update * almost working * correct group selection and rename n_group_exp * avoid large top_k and use argmax instead for now if we had something like argmax2 that would be equivalent, but this works fine until then * poke * skip group selection when there are no tokens * fix 1T conversion * hopefully fixed expert group selection third time's the charm? * make expert group selection generally available The new LLaDA2Moe model uses this method too, make it generally available regardless of architecture. * allow n_expert_groups to be 1 (Kimi K2) * address review suggestions
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@@ -139,7 +139,7 @@ models = [
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{"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
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{"name": "exaone4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
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{"name": "mellum", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
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{"name": "llada-moe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/LLaDA-MoE-7B-A1B-Base", },
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{"name": "bailingmoe2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", },
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{"name": "granite-docling", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
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]
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