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alt.freeweights.deepseek / deepseek-ai_DeepSeek-R1-0528-GGUF
From: bartowski <huggingface.co/bartowski>Newsgroups: alt.freeweights.deepseekSubject:

deepseek-ai_DeepSeek-R1-0528-GGUF

Date: 2026-11-15Commit: 708d712Signed: 2DC28CC7B26A283B ✓

Text generation model. Gguf weights, 8.03 TB in all. Published by bartowski under MIT.

deepseek8.03TBdeepseek-ai_DeepSeek-R1-0528-GGUFggufpermissive
FactsParamsnot statedSize8.03 TBFormatsggufTasktext generationLicenseMITFrom sourcepermissive
$ fw get bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF
# manifest sha256 2b22cf764f6a… · 8.03 TB · 222 files
$ fw verify ./deepseek-ai_DeepSeek-R1-0528-GGUF bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF
From sourceHosted (signed)bartowski/deepseek-ai_DeepSeek-R1-0528-GGUFLast check: 2026-11-15 04:47 UTC
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Quantized of DeepSeek-R1-0528.

Works with

Run14Convert1Evaluate13Scan12Apps58

Convert with

ggml
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Overview

Size
8.03 TB
Weights
147 GB (smallest of 24 GGUF variants)
Parameters
not stated
Formats
gguf
Task
text generation
License
MIT (permissive)
Files
222, each with a sha256
Publisher
bartowski
Added
2026-11-15
Section
alt.freeweights.deepseek
Lineage

Original release from the publisher.

Base model: deepseek-ai/DeepSeek-R1-0528

Where these bytes come from
Publisher at the pinned commit→You

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
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From sourcehuggingface.co2026-11-15 04:47 UTCpassed

Model card

From the publisher at commit 708d712. Shown as plain text with links; images open at the publisher.

Llamacpp imatrix Quantizations of DeepSeek-R1-0528 by deepseek-ai#

Using llama.cpp release b5524 for quantization.

Original model: https://huggingface.co/deepseek-ai/DeepSeek-R1-0528

Huge shoutout to Artus who helped with both conversion and imatrix calculation!

All quants made using imatrix option with dataset from here

Run them in LM Studio

Run them directly with llama.cpp, or any other llama.cpp based project

These quants were made with the changes from this PR for improved performance: https://github.com/ggml-org/llama.cpp/pull/12727

Prompt format#

<｜begin▁of▁sentence｜>{system_prompt}<｜User｜>{prompt}<｜Assistant｜>

Download a file (not the whole branch) from below:#

FilenameQuant typeFile SizeSplitDescription
DeepSeek-R1-0528-Q8_0.ggufQ8_0713.29GBtrueExtremely high quality, generally unneeded but max available quant.
DeepSeek-R1-0528-Q6_K.ggufQ6_K552.45GBtrueVery high quality, near perfect, recommended.
DeepSeek-R1-0528-Q5_K_M.ggufQ5_K_M478.34GBtrueHigh quality, recommended.
DeepSeek-R1-0528-Q5_K_S.ggufQ5_K_S463.03GBtrueHigh quality, recommended.
DeepSeek-R1-0528-Q4_1.ggufQ4_1421.04GBtrueLegacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
DeepSeek-R1-0528-Q4_K_M.ggufQ4_K_M409.23GBtrueGood quality, default size for most use cases, recommended.
DeepSeek-R1-0528-Q4_K_S.ggufQ4_K_S394.15GBtrueSlightly lower quality with more space savings, recommended.
DeepSeek-R1-0528-Q4_0.ggufQ4_0386.42GBtrueLegacy format, offers online repacking for ARM and AVX CPU inference.
DeepSeek-R1-0528-IQ4_NL.ggufIQ4_NL380.48GBtrueSimilar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
DeepSeek-R1-0528-IQ4_XS.ggufIQ4_XS359.98GBtrueDecent quality, smaller than Q4_K_S with similar performance, recommended.
DeepSeek-R1-0528-Q3_K_XL.ggufQ3_K_XL320.52GBtrueUses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
DeepSeek-R1-0528-Q3_K_L.ggufQ3_K_L319.71GBtrueLower quality but usable, good for low RAM availability.
DeepSeek-R1-0528-Q3_K_M.ggufQ3_K_M307.93GBtrueLow quality.
DeepSeek-R1-0528-IQ3_M.ggufIQ3_M307.88GBtrueMedium-low quality, new method with decent performance comparable to Q3_K_M.
DeepSeek-R1-0528-Q3_K_S.ggufQ3_K_S293.35GBtrueLow quality, not recommended.
DeepSeek-R1-0528-IQ3_XS.ggufIQ3_XS277.15GBtrueLower quality, new method with decent performance, slightly better than Q3_K_S.
DeepSeek-R1-0528-IQ3_XXS.ggufIQ3_XXS267.63GBtrueLower quality, new method with decent performance, comparable to Q3 quants.
DeepSeek-R1-0528-Q2_K_L.ggufQ2_K_L238.74GBtrueUses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
DeepSeek-R1-0528-Q2_K.ggufQ2_K237.83GBtrueVery low quality but surprisingly usable.
DeepSeek-R1-0528-IQ2_M.ggufIQ2_M215.04GBtrueRelatively low quality, uses SOTA techniques to be surprisingly usable.
DeepSeek-R1-0528-IQ2_S.ggufIQ2_S189.63GBtrueLow quality, uses SOTA techniques to be usable.
DeepSeek-R1-0528-IQ2_XS.ggufIQ2_XS188.41GBtrueLow quality, uses SOTA techniques to be usable.
DeepSeek-R1-0528-IQ2_XXS.ggufIQ2_XXS164.06GBtrueVery low quality, uses SOTA techniques to be usable.
DeepSeek-R1-0528-IQ1_M.ggufIQ1_M147.45GBtrueExtremely low quality, not recommended.

Embed/output weights#

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

Downloading using huggingface-cli#

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF --include "deepseek-ai_DeepSeek-R1-0528-Q4_K_M.gguf" --local-dir ./

If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:

huggingface-cli download bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF --include "deepseek-ai_DeepSeek-R1-0528-Q8_0/*" --local-dir ./

You can either specify a new local-dir (deepseek-ai_DeepSeek-R1-0528-Q8_0) or download them all in place (./)

ARM/AVX information#

Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.

Now, however, there is something called "online repacking" for weights. details in this PR. If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.

As of llama.cpp build b4282 you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.

Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to this PR which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.

Click to view Q4_0_X_X information (deprecated

I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.

Click to view benchmarks on an AVX2 system (EPYC7702)

modelsizeparamsbackendthreadstestt/s% (vs Q4_0)
qwen2 3B Q4_01.70 GiB3.09 BCPU64pp512204.03 ± 1.03100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64pp1024282.92 ± 0.19100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64pp2048259.49 ± 0.44100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64tg12839.12 ± 0.27100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64tg25639.31 ± 0.69100%
qwen2 3B Q4_01.70 GiB3.09 BCPU64tg51240.52 ± 0.03100%
qwen2 3B Q4_K_M1.79 GiB3.09 BCPU64pp512301.02 ± 1.74147%
qwen2 3B Q4_K_M1.79 GiB3.09 BCPU64pp1024287.23 ± 0.20101%
qwen2 3B Q4_K_M1.79 GiB3.09 BCPU64pp2048262.77 ± 1.81101%
qwen2 3B Q4_K_M1.79 GiB3.09 BCPU64tg12818.80 ± 0.9948%
qwen2 3B Q4_K_M1.79 GiB3.09 BCPU64tg25624.46 ± 3.0483%
qwen2 3B Q4_K_M1.79 GiB3.09 BCPU64tg51236.32 ± 3.5990%
qwen2 3B Q4_0_8_81.69 GiB3.09 BCPU64pp512271.71 ± 3.53133%
qwen2 3B Q4_0_8_81.69 GiB3.09 BCPU64pp1024279.86 ± 45.63100%
qwen2 3B Q4_0_8_81.69 GiB3.09 BCPU64pp2048320.77 ± 5.00124%
qwen2 3B Q4_0_8_81.69 GiB3.09 BCPU64tg12843.51 ± 0.05111%
qwen2 3B Q4_0_8_81.69 GiB3.09 BCPU64tg25643.35 ± 0.09110%
qwen2 3B Q4_0_8_81.69 GiB3.09 BCPU64tg51242.60 ± 0.31105%

Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation

Which file should I choose?#

Click here for details

A great write up with charts showing various performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

Credits#

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Thank you to LM Studio for sponsoring my work.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

Files

222 files · 8.03 TBWeights 147 GB (smallest of 24 GGUF variants). Sizes and sha256 from the signed manifest.
Show all 222 files with their sha256
FileSizesha256
.gitattributes29.4 KBdb8b4c0624bd6822da9aed2a69a682cefdf28b8e470444144720d4a74f4751c1
README.md13.9 KB2c6a6b91fffd05c7046378cadf3fd36c5e9f8f5384f3c30bbfa3fa8e31547451
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deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00012-of-00020.gguf36.7 GB7ec04233dc8c04ec9aa1f9e98f76c866647a904208ee185de55ed36c87b5c8e5
deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00013-of-00020.gguf36.7 GB9898e27d9c212a9ba42c7217c00863fe94a18b0c7d23a55076c4ff799035dff7
deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00014-of-00020.gguf36.7 GBfe23271f8766d8132dddb255c14b8130abfc4e9846cc168d17b627034a5b00e2
deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00015-of-00020.gguf36.7 GB3160ee5520dbbd01d77a4cf5799aaec11ab99b63fe1fa0f9e87edf29a192df9a
deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00016-of-00020.gguf36.7 GB400f300ca3396d516a070c2f4bdd27bf6dc50ef2fb1eb60cabfe2945a404c132
deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00017-of-00020.gguf36.7 GBdedaefd275b3223d156c8f5da8cc27ca96234ed6699ccd17ea7a5746413e1b26
deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00018-of-00020.gguf36.7 GB0cc2b825999b87dcdae1e1196733c1a8d5037acf3748630aefeece6891e588dd
deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00019-of-00020.gguf36.7 GB173932d0a7f1eb105e05368d4b0fca3e0ef1ee287d529a9fb98101f9fc16385c
deepseek-ai_DeepSeek-R1-0528-Q8_0/deepseek-ai_DeepSeek-R1-0528-Q8_0-00020-of-00020.gguf16.2 GB575b167f1e4e3ce780304c953c3ca28669cc16d1c0d6a566418312ace1e8af02
deepseek-ai_DeepSeek-R1-0528.imatrix987 MBc833dce37a84689c8aad4509f08d59d3ac842822774aa862c8a74ea4a88980d2
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
708d7126c3 pinned now2026-11-15 04:47 UTC222 files · 8.03 TB

Use it

$ fw get bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF
# manifest sha256 2b22cf764f6a… · 8.03 TB · 222 files
$ fw verify ./deepseek-ai_DeepSeek-R1-0528-GGUF bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF.json --dir ./deepseek-ai_DeepSeek-R1-0528-GGUF $ python3 fw.py verify ./deepseek-ai_DeepSeek-R1-0528-GGUF ./bartowski__deepseek-ai_DeepSeek-R1-0528-GGUF.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF --revision 708d7126c3dd74f547ad8bfa378a8d40fabb9a5c --local-dir ./deepseek-ai_DeepSeek-R1-0528-GGUF
llama.cppDownload one GGUF variant (deepseek-ai_DeepSeek-R1-0528-IQ1_M, 147 GB), then serve it.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF --include "deepseek-ai_DeepSeek-R1-0528-IQ1_M/*" --local-dir ./deepseek-ai_DeepSeek-R1-0528-GGUF $ llama-server -m ./deepseek-ai_DeepSeek-R1-0528-GGUF/deepseek-ai_DeepSeek-R1-0528-IQ1_M/deepseek-ai_DeepSeek-R1-0528-IQ1_M-00001-of-00004.gguf
OllamaMake an Ollama model from the downloaded file.
$ printf 'FROM ./deepseek-ai_DeepSeek-R1-0528-GGUF/deepseek-ai_DeepSeek-R1-0528-IQ1_M/deepseek-ai_DeepSeek-R1-0528-IQ1_M-00001-of-00004.gguf\n' > Modelfile $ ollama create deepseek-ai-deepseek-r1-0528-gguf -f Modelfile

License

Name
MIT License
Id
MIT
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
2c6a6b91fffd05c7046378cadf3fd36c5e9f8f5384f3c30bbfa3fa8e31547451
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: bartowski <huggingface.co/bartowski>Newsgroups: alt.freeweights.deepseekSubject: deepseek-ai_DeepSeek-R1-0528-GGUFDate: 2026-11-15Commit: 708d712Signed: 2DC28CC7B26A283B ✓
Upstream
bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF
Pinned commit
708d7126c3dd74f547ad8bfa378a8d40fabb9a5c
Manifest
deepseek-ai_DeepSeek-R1-0528-GGUF.json · .minisig
Manifest sha256
2b22cf764f6ae9a005d894ab312607d9bbc2b8993fb0dd41716db21aaf1efc0b
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:47 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF.json $ curl -O https://freeweights.allternit.com/manifests/bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF.json.minisig $ minisign -Vm deepseek-ai_DeepSeek-R1-0528-GGUF.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

deepseek-ai/DeepSeek-R1-0528 · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. deepseek-ai_DeepSeek-R1-0528-GGUF.json. Provenance. Takedown history: none.
Source
huggingface.co/bartowski/deepseek-ai_DeepSeek-R1-0528-GGUF

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