AI is happening. The future is updating.MiMo-V2.6-Flash-RL-cmf
> **Model release candidate.** All three weight files are uploaded and SHA-256 verified. > Text and greedy MTP checks pass; strict multimodal acceptance is still incomplete. > **Engine: Cortiq 0.7.7** · crates.io. Older published versions do not include this MiMo integration. **These are two complete file sets, not two copies of the 164-GB backbone.** Full includes the same text weights plus a **1.241-GB multimodal companion**. To upgrade from Text-only, download only the `.mm.cmf` file into the same folder. Text-only does not download or load the media towers. MiMo is a mixture-of-experts model with approximately **310 billion total parameters**, not a 15-billion-parameter checkpoint. Only a subset (roughly 15 billion parameters) is active per token, but **all experts must be stored**. Even a hypothetical uniform four-bit representation of 310 billion parameters would occupy about **155 GB**, before scales, metadata and higher-precision tensors. This conversion intentionally retains q8 and float tensors where needed.
> **Model release candidate.** All three weight files are uploaded and SHA-256 verified. > Text and greedy MTP checks pass; strict multimodal acceptance is still incomplete. > **Engine: Cortiq 0.7.7** · crates.io. Older published versions do not include this MiMo integration. **These are two complete file sets, not two copies of the 164-GB backbone.** Full includes the same text weights plus a **1.241-GB multimodal companion**. To upgrade from Text-only, download only the `.mm.cmf` file into the same folder. Text-only does not download or load the media towers. MiMo is a mixture-of-experts model with approximately **310 billion total parameters**, not a 15-billion-parameter checkpoint. Only a subset (roughly 15 billion parameters) is active per token, but **all experts must be stored**. Even a hypothetical uniform four-bit representation of 310 billion parameters would occupy about **155 GB**, before scales, metadata and higher-precision tensors. This conversion intentionally retains q8 and float tensors where needed.