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DeepSeek-V4-Flash-0731-DSpark-Drafter-GGUF

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metadata 
license: mit
library_name: gguf
base_model: deepseek-ai/DeepSeek-V4-Flash-0731
base_model_relation: quantized
tags:
  - gguf
  - deepseek-v4
  - dspark
  - speculative-decoding
  - draft-model
  - auxiliary-model

DeepSeek V4 Flash 0731 DSpark Drafter GGUF

This repository contains only the DSpark auxiliary drafter extracted from DeepSeek V4 Flash 0731. It is not a standalone language model and does not contain the target model weights.

The GGUF is built from the official deepseek-ai/DeepSeek-V4-Flash-0731 checkpoint using the standalone Python scripts included here. The complete source manifest, build recipe, structural parser, numeric verifier, dependency pin, and build results are published alongside the artifact.

Artifact

File Bytes SHA-256
DeepSeek-V4-Flash-0731-DSpark-Drafter-MXFP4-Q8_0.gguf 10,897,110,272 5c8a1ce75e4797c170e22ac65080281a1b694c77f2e4fa7a703fbf09c5de0e33
DeepSeek-V4-Flash-0731-DSpark-Drafter-Q2_K-Q8_0.gguf 6,971,241,728 a3f1156e1e4d2f46e1d4367b1bd09e6923adee61aecd41e880e233df33d30a7b

Both artifacts use GGUF v3 with the custom architecture deepseek_v4_flash_dspark_draft and contain the same 81 tensors. They differ only in the encoding of the nine routed-expert tensors:

Output type Tensors Transformation
F32 45 BF16 expanded exactly to F32, or source F32 bytes preserved
F16 2 BF16 Markov heads rounded to F16
Q8_0 25 FP8 E4M3 weights with E8M0 block scales dequantized, then quantized to Q8_0
MXFP4 9 Routed experts in the mxfp4-q8_0 artifact: FP4 nibbles and E8M0 scales repacked without requantization
Q2_K 9 Routed experts in the q2_k-q8_0 artifact: source MXFP4 decoded, then quantized to Q2_K

The MXFP4 transformation is bit-preserving. Q2_K, Q8_0, and F16 transformations are lossy and are checked against explicit numeric error bounds. This repository does not claim that either complete conversion is lossless.

Pinned source

  • Repository: deepseek-ai/DeepSeek-V4-Flash-0731
  • Revision: 9e165c30e2704aec5d9d593cce3eebd58bbef1cb
  • Input: only safetensors shards 46, 47, and 48
  • Total source size: 10,863,342,388 bytes

Exact file sizes and SHA-256 values are in manifest/source.json. The downloader and converter refuse any source that does not match them.

Reproduce

The reference build used Linux aarch64, Python 3.14.6, NumPy 2.5.1, uv 0.11.29, and Hugging Face CLI 1.24.0. The NumPy wheel is pinned by hash in requirements-linux-aarch64-py314.lock. The upstream checkpoint is public, so a Hugging Face token is not required for source download.

uv venv --python 3.14.6 .venv
uv pip install --python .venv/bin/python \
  --require-hashes --only-binary=:all: \
  -r requirements-linux-aarch64-py314.lock

.venv/bin/python scripts/download_sources.py --destination sources
.venv/bin/python -m unittest discover -s tests -v
.venv/bin/python scripts/reproduce.py \
  --sources sources \
  --recipe q2_k-q8_0 \
  --manifest-dir manifest \
  --repeat-check
sha256sum -c SHA256SUMS

Use --recipe mxfp4-q8_0 to reproduce the MXFP4/Q8_0 artifact instead.

--repeat-check performs a second clean conversion and requires its complete GGUF SHA-256 to match the verified first build byte for byte. The converter memory-maps the source shards and processes FP8 tensors in bounded bands. It does not require PyTorch, CUDA, safetensors, a GGUF package, or a GPU.

Verification

The included verifier checks pinned source hashes and tensor inventories, GGUF structure and metadata, exact F32/F16 bytes, exact MXFP4 repacking, Q2_K reconstruction error, and Q8_0 encoding plus reconstruction error against a fresh source decode.

Reference-build results:

Check Result
Tensor count 81
Exact F32/F16 tensors 47
Exact MXFP4 tensors 9
Q2_K tensors 9
Maximum Q2_K block-relative error 0.49072265625
Q2_K limit 0.5
Maximum Q8_0 block-relative error 0.00396728515625
Q8_0 limit 0.01
Repeat build byte-identical

Machine-readable observations are in the MXFP4/Q8_0 validation and build manifests, and the Q2_K/Q8_0 validation and build manifests.

Compatibility and limitations

  • This is an auxiliary drafter, not a complete checkpoint.
  • A consumer must explicitly support the deepseek_v4_flash_dspark_draft GGUF architecture and dspark.* tensor namespace defined by the checked-in recipe.
  • Generic GGUF support alone is insufficient. No compatibility with llama.cpp, Ollama, LM Studio, vLLM, or other runtimes is implied.
  • The drafter must be paired with a compatible DeepSeek V4 Flash 0731 target model. No universal quality, acceptance-rate, or speed claim is made across target quantizations or runtimes.
  • This is a community conversion, not an official DeepSeek release or endorsement.

DSpark configuration

The pinned source configuration defines block size 5, target layers 40, 41, and 42, Markov rank 256, hidden size 4096, and vocabulary size 129,280. These values are embedded in the GGUF metadata and validated during the build.

License and references

The source-derived weights remain under DeepSeek's MIT terms in LICENSE. The newly authored conversion and verification scripts are under LICENSE.code. The pinned checkpoint's upstream license and the DS4/GGML attribution in THIRD_PARTY_NOTICES.md provide the applicable notices.