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Lucebox/DeepSeek-V4.1-Flash-1017-ROCMFP-GGUF overview

DeepSeek V4.1 Flash 1017 for the Lucebox engine ROCMFP block formats Lucebox builds of DeepSeek's October 17 update of V4.1 Flash deepseek ai/DeepSeek V4.1 Fla…

ggufdeepseekluceboxrocmfpbase_model:deepseek-ai/DeepSeek-V4.1-Flash-1017base_model:quantized:deepseek-ai/DeepSeek-V4.1-Flash-1017license:mitregion:usimatrix

Runs locally from ~925.6 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

9 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
DeepSeek-V4.1-Flash-1017-DSpark-draft-MXFP4-Q8.ggufGGUFQ87.42 GBDownload
DeepSeek-V4.1-Flash-1017-DSpark-draft-Q2K-Q4K.ggufGGUFQ2K4.58 GBDownload
DeepSeek-V4.1-Flash-1017-ROCMFP23S-IM1017.ggufGGUFGGUF377.51 GBDownload
DeepSeek-V4.1-Flash-1017-ROCMFP23S.ggufGGUFGGUF377.51 GBDownload
DeepSeek-V4.1-Flash-1017-ROCMFP2S-IM1017.ggufGGUFGGUF356.42 GBDownload
DeepSeek-V4.1-Flash-1017-ROCMFP2S-MIX10-IM1017.ggufGGUFGGUF361.69 GBDownload
DeepSeek-V4.1-Flash-1017-ROCMFP2S-MIX10.ggufGGUFGGUF361.69 GBDownload
DeepSeek-V4.1-Flash-1017-ROCMFP2S.ggufGGUFGGUF356.42 GBDownload
DeepSeek-V4.1-Flash-1017-mmproj-BF16.ggufGGUFBF16925.6 MBDownload

Model Details

Model IDLucebox/DeepSeek-V4.1-Flash-1017-ROCMFP-GGUF
AuthorLucebox
Pipeline—
Licensemit
Base modeldeepseek-ai/DeepSeek-V4.1-Flash-1017
Last modified2026-10-18T12:24:42.000Z

Model README

---

license: mit

base_model: deepseek-ai/DeepSeek-V4.1-Flash-1017

base_model_relation: quantized

tags:

  • gguf
  • deepseek
  • lucebox
  • rocmfp

---

DeepSeek V4.1 Flash 1017 for the Lucebox engine (ROCMFP block formats)

Lucebox builds of DeepSeek's October 17 update of V4.1 Flash

(deepseek-ai/DeepSeek-V4.1-Flash-1017, revision 1bae0e10) for the

Lucebox DS4.1 engine (AMD Radeon AI PRO R9700 + Ryzen AI Max). Routed experts

in the engine's ROCMFP block formats; attention, shared experts and head in

Q8_0; both Engram tables embedded, read in place. Same recipes and the same

quality harness as our September V4.1 Flash files, re-measured against the

1017 model.

| file | routed experts | expert bytes | size | KL top-256 vs teacher | top-1 | ppl |

|---|---|---|---|---|---|---|

| teacher (fp4 checkpoint, 1017) | | 269 GiB | 510 GB | 0 | 100% | 8.56 |

| DeepSeek-V4.1-Flash-1017-ROCMFP2S.gguf | fp2s everywhere | 158 GiB | 383 GB | 0.265 | 78.6% | 9.77 |

| DeepSeek-V4.1-Flash-1017-ROCMFP2S-MIX10.gguf | fp2s, fp3 down on layers 0-9 | 164 GiB | 388 GB | 0.244 | 78.8% | 9.59 |

| DeepSeek-V4.1-Flash-1017-ROCMFP23S.gguf | fp2s gate/up, fp3 down everywhere | 179 GiB | 405 GB | 0.195 | 81.5% | 9.42 |

| DeepSeek-V4.1-Flash-1017-ROCMFP2S-IM1017.gguf | fp2s everywhere, imatrix collected on 1017 | 158 GiB | 383 GB | 0.253 | 78.7% | 9.78 |

| DeepSeek-V4.1-Flash-1017-ROCMFP2S-MIX10-IM1017.gguf | MIX10, imatrix collected on 1017 | 164 GiB | 388 GB | 0.238 | 79.2% | 9.64 |

| DeepSeek-V4.1-Flash-1017-ROCMFP23S-IM1017.gguf | ROCMFP23S, imatrix collected on 1017 | 179 GiB | 405 GB | 0.194 | 81.3% | 9.41 |

For reference, the September files scored KL 0.266 (ROCMFP2S), 0.246

(MIX10) and 0.204 (fp2s with fp3 down everywhere) against the September model

(teacher ppl 8.45) on the same tokens.

Which one: ROCMFP2S is what the 128 GB Lucebox installs (the hottest

experts on the R9700, the rest on the Ryzen AI Max and the SSD). MIX10 is the

better file where it fits resident (192 GB boards). ROCMFP23S is the best

quality of the three, for two-box setups and boards with room for 179 GiB of

experts. The -IM1017 files are the same recipes calibrated on the 1017

weights; prefer them (Lucebox Manage installs ROCMFP2S-IM1017).

Drafter

DeepSeek-V4.1-Flash-1017-DSpark-draft-MXFP4-Q8.gguf (7.4 GiB, sha256 in drafter/): the

draft block DeepSeek ships inside the 1017 checkpoint (mtp.*, three stages,

128 routed experts top-3), routed experts repacked bit-exactly to MXFP4, dense

projections Q8_0. Use it with these targets; a drafter from the September

checkpoint proposes tokens the 1017 target accepts less often.

DeepSeek-V4.1-Flash-1017-DSpark-draft-Q2K-Q4K.gguf (4.6 GiB): the same

drafter with Q2_K routed experts and Q4_K dense weights, for the 192 GB

layout (--profile ds41-gorgon), where it leaves the R9700 room for larger

prefill chunks.

Image input

DeepSeek-V4.1-Flash-1017-mmproj-BF16.gguf (0.90 GiB): the 1017

checkpoint's vision encoder (32-layer ViT and the aligner into the 5120-wide

model), its 266 tensors copied byte for byte in BF16 by the engine's

server/tools/export_ds4v_mmproj.py, with the checkpoint revision recorded.

The 1017 update retrained the encoder with the language weights: pair it only

with these 1017 targets (the engine refuses an encoder from another

checkpoint). Add --mmproj DeepSeek-V4.1-Flash-1017-mmproj-BF16.gguf to the

command below; images then go in as OpenAI image_url parts.

Run

luce_server DeepSeek-V4.1-Flash-1017-ROCMFP2S.gguf \
  --profile ds41-lucebox \
  --draft DeepSeek-V4.1-Flash-1017-DSpark-draft-MXFP4-Q8.gguf

With 192 GB of unified memory use --profile ds41-gorgon with the Q2K-Q4K

drafter, where every expert stays resident. Lucebox Manage installs the files and picks the layout for the

box. Engine and guide: https://github.com/Luce-Org/lucebox (server/docs/DS41.md).

Formats and measurement

Q2_0_ROCMFP2 (ggml type 107, 10 bytes per 32 weights, 2.5 bits, codebook

{-1, 0, 1, 2} times a ue4m3 scale per 16 weights). fp2s files use type

107 with bit 7 of a half-block's scale byte meaning "mirror the codebook"; a

reader that ignores that bit produces garbage, so use an engine build that

honors it. Q3_0_ROCMFPX (type 104, 14 bytes per 32, 3.5 bits) on the down

projections of the MIX10 file's first ten layers.

Every block's scale is chosen by an exhaustive search minimizing the

activation-weighted squared error, weighted by an importance matrix (262,144

calibration tokens; the September V4.1 imatrix for the three plain files,

an imatrix collected on the 1017 weights for the -IM1017 files: 196,608

tokens for ROCMFP2S-IM1017, the completed 262,144 tokens in imatrix/ for

MIX10-IM1017 and ROCMFP23S-IM1017). KL is measured on 8,184

held-out tokens against the unquantized 1017 model through the same reference forward pass; eval/ holds

the teacher log-probs and the tokens, results/ the raw scores and the file

headers, imatrix/ the calibration statistics, so anyone can rescore a file.

Toolkit: the Lucebox repo, lucebox_training/quantization.

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