steadfastgaze/DeepSeek-V4-Flash-0731-Coder-56.8GB-MoEspressoV2

DeepSeek V4 Flash 0731 Coder, compact MoEspresso package

This is a 56.869 GB coding-specialized MoEspresso package derived from DeepSeek-V4-Flash-0731. Its model safetensors occupy 56.831 GB, 32.63% less than the 84.355 GB MoEspresso 2.37 bpw package. The package removes about 80B parameters, leaving 204B total, with about 13B active per token.

You can see a screen recording of it writing a small C compiler here:

https://youtu.be/XiwSilmV8B0

The package requires MoEspresso 2.1 or newer. MoEspresso is the only engine that serves it. llama.cpp, transformers, and mlx-lm will not load these files.

brew install steadfastgaze/tap/moespresso
moespresso serve /path/to/package

This package is intended for coding and coding-agent workloads. It is not a smaller general-purpose replacement for the 2.37 bpw package. The measurements below show both the coding retention and the general-text regression.

Quality

Lower NLL and perplexity are better. The code panel contains 12 fixed 2,048-token windows and 24,564 next-token targets from code.utf8.

package	model safetensors	code mean NLL	code perplexity	selected-token agreement with 2.37 bpw
This Coder package	56.831 GB	1.01758	2.7665	88.55%
MoEspresso 2.37 bpw	84.355 GB	0.88582	2.4250	reference

An unpruned all-IQ1 no-REAP research baseline was researched, although not published, and relative to that baseline, pruning and specialization increased WikiText mean NLL by 0.25170 while reducing code mean NLL by 0.10250. Extreme IQ1 quantization accounts for most of the broad-text loss from the 2.37 bpw package. The subsequent specialization trades additional broad-text quality for better code modeling.

The broad-text and official-continuation panels quantify the resulting product boundary.

package	WikiText mean NLL	WikiText perplexity	API-continuation mean NLL	API first-token agreement
This Coder package	2.41630	11.2043	1.23133	48/100
MoEspresso 2.37 bpw	1.71466	5.5548	0.39634	66/100

The WikiText panel uses the first 32 contiguous 2,048-token windows from the WikiText-2 test split and scores the last 1,023 tokens of each window, for 32,736 targets. The API panel uses 100 prompts and 2,313 provider-selected continuation tokens from the official DeepSeek 0731 model. Each local package is teacher-forced under the stored continuation. This is not free-running API agreement.

The rendered reasoning and tool validation panel uses held-out conversations converted to DeepSeek's prompt and tool-call format.

category	positions	Coder NLL	2.37 bpw NLL	Coder selected-token accuracy	2.37 bpw accuracy
All rendered targets	8,188	1.33567	1.31694	75.67%	75.95%
Reasoning	1,806	1.46925	1.47384	66.11%	66.06%
Structured tool calls	1,880	0.46838	0.39007	90.05%	90.69%
Tool-result history	850	1.12205	1.11188	76.59%	78.59%

The package also passes additional checks, e.g.:

check	scope	result
Long-context fact recall	16 fixed facts in a 30,000-token-class prompt	16/16 exact match
Coding capability	16 private tests	16/16 tests

These panels were used during package development. They characterize the final artifact but are not untouched post-selection benchmarks.

Recipe

The three hash-routed layers keep all 256 experts. Each of the 40 learned-router layers keeps 176 of 256 experts, for 7,808 retained expert rows in total. The served router still selects six experts per token.

Expert retention and precision were selected together. Of the 129 routed projection cells, 32 use IQ1_S_R4, 64 use IQ2_KS, and 33 use IQ2_K. The denser model weights use q6_K. The layer-12 down projection is explicitly promoted; short closure screens and full autoregressive reasoning runs showed that promotion count alone did not predict sustained-thinking quality.

The package does not include a drafter. A separately distributed DSpark sidecar can be checked and served with MoEspresso 2.1:

moespresso verify /path/to/package --drafter /path/to/dspark-sidecar
moespresso serve /path/to/package --drafter /path/to/dspark-sidecar

Corpus and specialization

Weight quantization used Bartowski's calibration_datav5 and exllamav3's technical.utf8.

Expert retention used general windows from calibration_datav5, coding windows from exllamav3's code.utf8, unrelated casual chat, and the conversations train split of Reasoning and Tool Calling.

WikiText-2 was used only for evaluation.

Verification

After downloading, verify the complete directory before serving it:

moespresso verify /path/to/package

Credits

The base model was trained and released by DeepSeek. The IQ_K formats were designed by Iwan Kawrakow and are served through mlx-iqk. The dense q6_K path uses mlx-kquant. Construction data includes material published by Bartowski and turboderp-org. MoEspresso runs on Apple's MLX.

The complete third-party provenance is recorded in the MoEspresso repository's THIRD-PARTY-NOTICES and in the notices distributed by its dependencies.