DeepSeek-V4-Flash-0731 REAP K160
This is an experimental, structurally validated REAP expert-pruned checkpoint of deepseek-ai/DeepSeek-V4-Flash-0731. It keeps 160 of 256 routed experts in every MoE scope while preserving top-6 routing, shared experts, attention, embeddings, output head, mHC, compressor, and indexer tensors.
The checkpoint is intended for memory-constrained DeepSeek-V4 research and inference. It is not an official DeepSeek release, and pruning can reduce quality. Evaluate it on your own coding, reasoning, tool-use, and long-context workloads before production use.
What was pruned
| Item | Value |
|---|---|
| Source revision | 9e165c30e2704aec5d9d593cce3eebd58bbef1cb |
| Original routed experts | 256 per MoE scope |
| Retained routed experts | 160 per MoE scope |
| Routed experts removed | 37.5% |
| Router top-k | 6, unchanged |
| Backbone MoE layers | 43 |
| DSpark MTP blocks | 3, mapped to backbone layers 40, 41, and 42 |
| Safetensors shards | 48 |
| Tensor payload | 107,803,320,952 bytes |
Each retained expert is physically compacted into a contiguous 160-expert tensor. Learned router weights and biases are sliced to the same retained IDs. For the first three hash-routed layers, token-to-expert tables are remapped to the nearest retained current-revision router row by cosine similarity.
Observation provenance
This release transfers expert rankings from a prior DeepSeek-V4-Flash REAP observation run; it is not a fresh observation of the 0731 weights.
- Observation dataset:
0xSero/deepseek-v4-flash-reap-observations-v2 - Snapshot:
partial-v2-21289 - Rows used: 21,289
- Prior source revision:
deepseek-ai/DeepSeek-V4-Flash@60d8d70770c6776ff598c94bb586a859a38244f1 - Coverage: coding, math, science, tool calling, and agentic tool trajectories; every expert was observed
Before transferring the ranking, all 43 old router matrices were aligned against the 0731 checkpoint. Every old expert row had the same expert ID as its nearest current-revision row. The weakest layer-mean same-index cosine was 0.9923683405; the weakest individual row was 0.9460793734. All three hash-routing tables were bit-identical. These results support identity transfer, but they do not make it equivalent to a fresh 0731 observation.
Validation
Structural validation passed with:
- 48/48 readable safetensors shards
- 45,821 tensors and exact index coverage
- 46/46 MoE scopes at K160
- 92 exact router-tensor comparisons
- 552 exact retained-expert old-to-new comparisons
- 12 exact protected-tensor comparisons
- zero structural failures
The machine-readable build and validation records are included as REAP_MANIFEST.json, reap_plan.json, and files under validation/.
Runtime validation was performed on one NVIDIA DGX Spark / GB10 using the Anemll DeepSeek-V4 vLLM preview image, with CUDA graphs enabled. The bundled CUDA sqrtsoftplus router supports only a fixed set of expert counts, so K160 requires routing unsupported expert counts through vLLM's included pure-Torch router fallback. Expert execution remains on the B12X MXFP4 MoE backend.
The backbone-only API smoke passed at 8,192-token configured context: /v1/models became ready, the exact-answer probe returned PRUNED_OK in 6.19 seconds, and the coding probe returned return a + b in 1.60 seconds. Both stopped normally. The requests intentionally had no output-token cap. DSpark MTP was not enabled for this smoke.
Serving notes
Use a DeepSeek-V4-capable runtime. Generic Transformers releases may not understand the model's custom MXFP4, sparse-attention, tokenizer, and DSpark MTP components. The exact runtime smoke configuration and result are recorded under validation/.
For the tested vLLM preview lineage, patch vllm_topk_softplus_sqrt so expert counts outside the fused kernel's supported set (16, 32, 64, 128, 192, 256, 320, 384, 512) call the adjacent _topk_softplus_sqrt_torch implementation. Do not change the checkpoint back to 256 experts: that would invalidate the tensors and routing map.
Limitations
- Expert importance came from a closely aligned earlier checkpoint, not a fresh
0731observation. - Structural and smoke validation do not establish benchmark parity with the unpruned model.
- This is an aggressive 37.5% routed-expert prune. Coding preservation is plausible because coding and agentic data were included, but must be measured rather than assumed.
- The included three DSpark MTP blocks were pruned consistently; MTP runtime compatibility should be validated separately from backbone generation.
Credits and license
Thanks to DeepSeek for the base model, Cerebras Research for REAP, the vLLM contributors, and the Anemll/DGX Spark community for the DeepSeek-V4 Spark runtime work.
This derivative follows the source repository's MIT license. See LICENSE for the full text.