DeepSeek's V4-Flash-Vision repo has no weights in it

DeepSeek posted a repo called DeepSeek-V4-Flash-Vision-Exp to Hugging Face, and the 228-upvote r/LocalLLaMA thread around it reads like an open-weights release. It isn't one.

What's actually in the repo is a tokenizer, a prompt-encoding reference, and a minimal PyTorch implementation: the vision encoder and aligner, DFlash attention, the MoE routing, Hyper-Connections, and the DSpark forward path. No weight files. The model behind it is a 284B-parameter MoE built from twenty 13B experts, and it's been served through DeepSeek's API only since August 21, at the same price as V4-Flash: $0.14 per 1M cache-miss input tokens, $0.28 per 1M output tokens. Images cost up to 384 tokens each with no separate per-image fee.

For anyone hoping to run this locally, the repo answers a different question. You can read exactly how DFlash attention and Hyper-Connections are wired together in code, which is more architectural detail than most labs publish for a model they're keeping API-only. You just can't load the weights and run inference on your own hardware.

The mismatch matters because "open-weights" and "open architecture" get treated as the same headline on social platforms, and they're not. A repo with a tokenizer and reference code costs a lab nothing in inference revenue and buys real goodwill in threads that don't check the file list. If DeepSeek wanted this open the way past DeepSeek releases have worked, the weights would already be there. Watch whether a full weight release follows, or whether this pattern, architecture code without weights, becomes how labs test community interest before deciding if an API-only model is worth opening up.