A smaller EAQ v1 (EXL3 format) quantization of MiniMaxAI/MiniMax-M3.1-Flash: 3.064 bits per weight on average over the quantized body, with every routed expert stored at 3 bit. The text model is about 167 GB (155 GiB); with the dspark draft and the vision tower the repo is about 171 GB (159 GiB). The indexer chooses which tokens attention reads, and the router chooses which experts run, so both stay at source precision in every layer. Compared with the 3.55 bpw pack, the layers that were 4 bit (3-16 and 46-59) are now 3 bit, and the head is 8 bit instead of 6. — Inventory: 89,832 text tensors plus 523 vision tensors in model.safetensors.index.json; the 22,309 quantized linears and the 8-bit head are present, widths match the recipe, indexer FP16 and router FP32 in every layer, 384 expert tensors per MoE layer, all float tensors finite. — Payload hashes: every compiled tensor equals its source tensor file by dtype, shape and sha256. — Extras: the vision shard and the dspark/ files are byte-identical to the base model’s. — Conversion gates: all structural gates passed at every barrier (layers 16, 31, 45 and the end). — Quality vs the original model. The teacher-forced KL / top-1…
Модальности:
Генерация текста
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Диалог / чат
Задача: Генерация текста
Автор: Mia-AiLab
Теги: exllamav3, minimax_m3_vl, exl3, eaq, quantized, moe, minimax, conversational
Лайков: 3 | Загрузок: 308
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
