256-expert calibration statistics (REAM line)

README.md

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+---
+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.

domains.json

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+{
+ "domains": ["code", "agentic", "multilingual_chat", "math", "russian", "roleplay", "general_chat", "long_docs"],
+ "shares": {
+  "code": 0.34,
+  "agentic": 0.22,
+  "multilingual_chat": 0.15,
+  "math": 0.08,
+  "general_chat": 0.06,
+  "roleplay": 0.06,
+  "russian": 0.05,
+  "long_docs": 0.04
+ },
+ "total_tokens": 5000000,
+ "heldout_tokens": 200000,
+ "note": "domain order matches the saliency axis in layer_XX.npz"
+}

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