Tylogi/DeepSeek-V4-Flash-0731-EW-MFQ

Run DeepSeek-V4-Flash-0731 with TyloQuant MFQ

TyloQuant MFQ provides neuron-anchored mixed-format quantization and high-fidelity inference for large language models.

• This repository contains MFQ-quantized weights derived from the official DeepSeek-V4-Flash-0731 release.
• MFQ combines NINT, NVQ/NPQ and NEPQ formats with expert-wise precision allocation for MoE models.
• The released 77.519 GiB S tier records 0.313576 Mean KLD and 82.2913% same-top on the complete official-0731 WikiText-2 evaluation.
• See the project documentation for runtime, format and deployment instructions.

_{Full WikiText-2 evaluation against official DeepSeek-V4-Flash-0731 BF16 logits: ctx=512, 573 chunks and 146,115 scored tokens. Lower Mean KLD is better. Complete results and protocol.}

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DeepSeek-V4-Flash-0731

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Technical Report👁️

Introduction

DeepSeek-V4-Flash-0731 is the official release of DeepSeek-V4-Flash, superseding the preview version, with substantially enhanced agentic capabilities. It has the same model structure as DeepSeek-V4-Flash-DSpark, i.e. it comes with a speculative decoding module attached.

DeepSeek-V4-Flash-0731 outperforms DeepSeek-V4-Pro (Preview) on benchmarks listed below despite its far smaller activated parameter count, and is broadly competitive with the strongest proprietary models available.

Benchmark	DeepSeek-V4-Flash-0731	DeepSeek-V4-Flash (Preview)	DeepSeek-V4-Pro (Preview)	GLM-5.2	Opus-4.8
Terminal Bench 2.1	82.7	61.8	72.1	81.0	85.0
NL2Repo	54.2	39.4	38.5	48.9	69.7
Cybergym	76.7	38.7	52.7	-	83.1
DeepSWE	54.4	7.3	12.8	46.2	58.0
Toolathlon-Verified	70.3	49.7	55.9	59.9	76.2
Agents' Last Exam	25.2	15.8	16.5	23.8	25.7
AutomationBench Public	25.1	10.8	12.8	12.9	27.2
DSBench-FullStack †	68.7	37.0	41.8	61.8	71.6
DSBench-Hard †	59.6	25.8	31.1	54.5	71.7

Notes:

1. For the Code Agent tasks among the public benchmarks above, DeepSeek-V4-Flash-0731 is evaluated with the minimal mode of DeepSeek Harness (to be released) as the agent framework, using the max reasoning effort level with temperature = 1.0, top_p = 0.95.
2. † DSBench-FullStack is an internal full-stack development test set; DSBench-Hard is an internal test set of difficult coding-agent problems.

License

This repository and the model weights are licensed under the MIT License.

Citation

@misc{deepseekai2026deepseekv4,
      title={DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence},
      author={DeepSeek-AI},
      year={2026},
}