DeepSeek-V4-Flash-0731-REAM96-111B-DS4-GGUF

DeepSeek V4 Flash — REAM96 (111B) · DS4 Q2

A 2-bit build of DeepSeek-V4-Flash-0731-REAM96-111B — DeepSeek-V4-Flash with 96 of the original 256 experts per layer, sized for 48 GiB configurations. Quantized with the standard DS4 recipe (2-bit experts, 8-bit attention) and a fresh importance matrix.

[!IMPORTANT] This is a DS4-specific GGUF. Run it with the DS4 fork — the 96-expert topology needs its variable expert count support. Generic llama.cpp will not load this file.

[!WARNING] Live smoke testing passed 5/10 scenarios on the first run. Independent reruns show the failures (Russian wordplay, multi-turn, English → Russian code-switching, Tool calling (DSML), Long-dialog focus (drift check), Tool call → code chain) are intermittent, not absolute — see the Stability column below for per-scenario pass rates. Multilingual chat, reasoning and long dialogs are consistently healthy. The full-precision native checkpoint may behave better — 2-bit quantization hits agentic behavior hardest.

Files

Run

Plain:

./ds4 -m ream96.gguf -c 8192

With DSpark speculative decoding (faster generation, more memory):

./ds4 -m ream96.gguf --mtp dspark.gguf --dspark -c 8192

DSpark fits alongside on 64 GiB hosts, but in our Apple Silicon runs it gave no measurable speedup (12.9 vs 13.0 tok/s) — treat it as experimental.

How it was made

One pruning step, straight from the original — no cascading. Expert importance was measured by running deepseek-ai/DeepSeek-V4-Flash-0731 over a ~5-million-token calibration mix (multi-turn dialogs, thinking and direct modes, rendered with the model's own chat encoder). The strongest experts of every domain were protected from pruning, the survivors were carried over byte-identical, and the router was re-balanced to keep the original selection behavior.

This line replaces the earlier cascaded REAM builds (now archived under -exp names), which degraded badly in multi-turn use.

Smoke results

Every scenario is a live multi-turn conversation run end-to-end on the DS4 runtime (raw evidence ships in SMOKE_REPORT.json).

Stability = pass rate over independent reruns of the scenarios that failed the first run; passing scenarios were not re-run.

Limitations

• Needs the DS4 fork; not a generic llama.cpp file.
• 2-bit quantization is aggressive: expect the native checkpoint to be smarter than this build, especially on agentic tool use.
• Memory use grows with context length and DSpark; the sizes above are the files alone.