| --- |
| license: mit |
| pretty_name: DeepSeek-V4-Flash-0731 REAM calibration statistics |
| tags: |
| - mixture-of-experts |
| - pruning |
| - expert-merging |
| - deepseek-v4 |
| - interpretability |
| --- |
| |
| # DeepSeek-V4-Flash-0731 — expert calibration statistics (REAM line) |
|
|
| Layerwise routed-expert statistics of |
| [`deepseek-ai/DeepSeek-V4-Flash-0731`](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731) |
| (43 MoE layers × 256 experts), collected by running the full model over a |
| ~4.9M-token multi-domain calibration mix (multi-turn dialogs, thinking and |
| direct modes, rendered with the model's own chat encoder). These are the |
| statistics behind the REAM144/96 release line — published so that expert |
| selection, pruning, merging and routing research can start WITHOUT the |
| expensive H100 collection pass. |
|
|
| ## Files |
|
|
| - `layer_XX.npz` (one per MoE layer): |
| - `saliency [8, 256]` — per-domain REAP-style saliency |
| `S_i = f_i · E[gate_i · ‖expert_i(x)‖₂ | i ∈ Top-6]`; domain order in |
| `domains.json`; |
| - `freq [256]` — Top-6 selection counts over the mix; |
| - `coact [256, 256]` — joint Top-6 co-activation counts; |
| - `load [256]` — historical load share (sums to 1); |
| - `gsum [256]`, `token_count` — auxiliary. |
| - `imatrix_raw.npz` — squared-input accumulators per expert |
| (`gate [43, 256, H]`, `down [43, 256, I]`, `calls [43]`) — the raw material |
| for llama.cpp-style importance matrices of ANY expert subset or merge |
| (weighted sums of member rows; see `emit_imatrix_merged.py` in the release |
| pipeline). |
| - `domains.json` — domain names (canonical order used by the `saliency` |
| axis) and mix shares. |
| - `nll_heldout.json` — the source model's NLL on the held-out slice of the |
| same mix, collected during the pass (a reference point for compressed |
| variants). |
| |
| ## What these enable |
|
|
| - Reproducing/improving the published selections (each release carries its |
| `SELECTION.json`); |
| - Expert pruning/merging experiments at zero collection cost; |
| - Routing analyses: language/domain specialization by depth, co-activation |
| cluster structure, load distributions. |
|
|
| ## Collection notes |
|
|
| Single pass, batch 6 × 4096 tokens, deterministic packer; the calibration |
| texts themselves are NOT included and are not recoverable from these |
| aggregates. Related models: the REAM144/96 line and its merge variants |
| (see the collection on this profile). |
|
|
| MIT, following the source model. |
|
|
