A 2-bit build of DeepSeek-V4-Flash-0731-REAM144-163B — DeepSeek-V4-Flash with 144 of the original 256 experts per layer, sized to run fully resident on a 64 GiB Mac with room for 8k context. 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 144-expert topology needs its variable expert count support. Generic llama.cpp will not load this file.

[!WARNING] Live smoke testing passed 7/10 scenarios on the first run. Independent reruns show the failures (Tool calling (DSML), Code refactoring, 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

File	Size	What it is
`ream144.gguf`	49.3 GB	The model
`dspark.gguf`	5.7 GB	Optional speculative decoding (DSpark)
`imatrix.dat`	0.2 GB	Importance matrix used for this quant
`SMOKE_REPORT.json`	—	Raw smoke-test evidence

Run

Plain:

./ds4 -m ream144.gguf -c 8192

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

./ds4 -m ream144.gguf --mtp dspark.gguf --dspark -c 8192

Adding DSpark pushes the total past a 64 GiB budget — measured on a 64 GiB Mac it slows prefill ~10× and can thrash generation; use it on larger hosts only.

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.

Calibration domain	Share
Code	35%
Agentic / tool use	19%
Multilingual chat	16%
Math	8%
General chat	6%
Roleplay	6%
Russian	5%
Long docs	4%

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).

Scenario	First run	Stability (reruns)
Russian wordplay, multi-turn	✅	—
English → Russian code-switching	✅	—
Code Q&A over a 4k-token file	✅	—
Tool calling (DSML)	❌	5/10
Russian multi-turn reasoning	✅	—
Spanish creative writing	✅	—
Code refactoring	❌	8/10
Chinese summarization	✅	—
Long-dialog focus (drift check)	✅	—
Tool call → code chain	❌	5/10

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.

WaveCut/DeepSeek-V4-Flash-0731-REAM144-163B-DS4-GGUF

DeepSeek V4 Flash — REAM144 (163B) · DS4 Q2

Files

Run

How it was made

Smoke results

Limitations

WaveCut/
DeepSeek-V4-Flash-0731-REAM144-163B-DS4-GGUF