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audreyt/deepseek-v4.1-flash-1017-gguf overview

DeepSeek V4.1 Flash 1017 GGUF for DwarfStar Calibrated Q2 weights and the matching vision encoder for DwarfStar https://github.com/antirez/ds4 , converted from…

ggufdeepseekmetalbase_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

2 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
DeepSeek-V4.1-Flash-1017-Q2.ggufGGUFQ2340.60 GBDownload
DeepSeek-V4.1-Flash-1017-Vision.ggufGGUFGGUF925.6 MBDownload

Model Details

Model IDaudreyt/deepseek-v4.1-flash-1017-gguf
Authoraudreyt
Pipeline—
Licensemit
Base modeldeepseek-ai/DeepSeek-V4.1-Flash-1017
Last modified2026-10-19T00:44:22.000Z

Model README

---

license: mit

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

tags:

- gguf

- deepseek

- metal

---

DeepSeek V4.1 Flash 1017 GGUF for DwarfStar

Calibrated Q2 weights and the matching vision encoder for

DwarfStar, converted from

deepseek-ai/DeepSeek-V4.1-Flash-1017 with the V4.1 recipe in gguf-tools.

| Download target | File | File size | Main weights |

| --- | --- | ---: | ---: |

| ds41f-1017-q2 | DeepSeek-V4.1-Flash-1017-Q2.gguf | 340.60 GiB | 151.77 GiB |

| ds41f-1017-vision | DeepSeek-V4.1-Flash-1017-Vision.gguf | 0.90 GiB | Separate vision encoder |

The language GGUF includes 188.83 GiB of native FP8 Engram tables. DwarfStar

reads the needed rows directly from disk, so keep the GGUF on a fast local SSD.

Download and Run

The download targets and the 1017 vision encoder need

audreyt/ds4: it pairs a vision encoder with

the language GGUF by source revision, while upstream accepts only the original

V4.1 encoder.

./download_model.sh ds41f-1017-q2
./download_model.sh ds41f-1017-vision
./ds4 -m gguf/DeepSeek-V4.1-Flash-1017-Q2.gguf \
  --vision gguf/DeepSeek-V4.1-Flash-1017-Vision.gguf --ssd-streaming --ctx 32768

This encoder does not load with the original V4.1 Flash GGUF, and the original

encoder does not load with this one.

On a 128 GB M5 Max with --ssd-streaming, ds4-bench measured 202-229 t/s

prefill and about 16 t/s generation from 4K to 16K context.

Quantization

IQ2_XXS routed gate/up experts and Q2_K down experts; Q8 attention projections,

shared experts and output; F16/F32 tensors elsewhere; native Engram data

unchanged. Routed experts use a 32,768-token activation imatrix collected with

the DwarfStar Metal collector on a weight-energy bootstrap GGUF; it activated

93.84% of the 15,360 layer-experts, and the rest keep the weight-energy

fallback. The GGUF was quantized from the original safetensors, not requantized.

deepseek41_validate_gguf.py --payload --imatrix passed: complete layout, every

non-expert tensor, sampled routed experts and Engram rows match the source.

The encoder is copied byte for byte from the same checkpoint.

Source: deepseek-ai/DeepSeek-V4.1-Flash-1017 at

1bae0e1088993b9b82de08f59de5be5a34e42170. See the included upstream MIT license.

| File | SHA-256 |

| --- | --- |

| DeepSeek-V4.1-Flash-1017-Q2.gguf | 753ba9c55ed56e0ca0bf8c87876367349027452629f8f4975cdb09d50189ac07 |

| DeepSeek-V4.1-Flash-1017-Vision.gguf | 51e4525b67cdaf1f34c569118b268e06466233e9d5dc13442fc65170dbb3fd21 |

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