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metadata
quantized_by: bartowski
pipeline_tag: text-generation
language:
  - en
  - ar
  - cs
  - de
  - es
  - fr
  - it
  - ja
  - ko
  - nl
  - pt
  - zh
tags:
  - language
  - granite
  - embeddings
  - multilingual
license: apache-2.0
base_model: ibm-granite/granite-embedding-278m-multilingual
model-index:
  - name: ibm-granite/granite-embedding-278m-multilingual
    results:
      - task:
          type: Retrieval
        dataset:
          name: Miracl (en)
          type: miracl/mmteb-miracl
          config: en
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.45557
          - type: ndcg_at_10
            value: 0.49372
          - type: ndcg_at_100
            value: 0.5728
          - type: ndcg_at_1000
            value: 0.59187
          - type: ndcg_at_20
            value: 0.52863
          - type: ndcg_at_3
            value: 0.43969
          - type: ndcg_at_5
            value: 0.45551
          - type: recall_at_1
            value: 0.21785
          - type: recall_at_10
            value: 0.59513
          - type: recall_at_100
            value: 0.85785
          - type: recall_at_1000
            value: 0.96041
          - type: recall_at_20
            value: 0.69357
          - type: recall_at_3
            value: 0.40403
          - type: recall_at_5
            value: 0.48499
      - task:
          type: Retrieval
        dataset:
          name: Miracl (ar)
          type: miracl/mmteb-miracl
          config: ar
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.57459
          - type: ndcg_at_10
            value: 0.64238
          - type: ndcg_at_100
            value: 0.6867
          - type: ndcg_at_1000
            value: 0.6951
          - type: ndcg_at_20
            value: 0.66455
          - type: ndcg_at_3
            value: 0.58162
          - type: ndcg_at_5
            value: 0.60831
          - type: recall_at_1
            value: 0.38064
          - type: recall_at_10
            value: 0.75098
          - type: recall_at_100
            value: 0.91203
          - type: recall_at_1000
            value: 0.96706
          - type: recall_at_20
            value: 0.81978
          - type: recall_at_3
            value: 0.58618
          - type: recall_at_5
            value: 0.66353
      - task:
          type: Retrieval
        dataset:
          name: Miracl (bn)
          type: miracl/mmteb-miracl
          config: bn
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.60341
          - type: ndcg_at_10
            value: 0.68055
          - type: ndcg_at_100
            value: 0.72008
          - type: ndcg_at_1000
            value: 0.72716
          - type: ndcg_at_20
            value: 0.69914
          - type: ndcg_at_3
            value: 0.60805
          - type: ndcg_at_5
            value: 0.64486
          - type: recall_at_1
            value: 0.37948
          - type: recall_at_10
            value: 0.80609
          - type: recall_at_100
            value: 0.94305
          - type: recall_at_1000
            value: 0.98625
          - type: recall_at_20
            value: 0.86141
          - type: recall_at_3
            value: 0.61095
          - type: recall_at_5
            value: 0.71316
      - task:
          type: Retrieval
        dataset:
          name: Miracl (de)
          type: miracl/mmteb-miracl
          config: de
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.45574
          - type: ndcg_at_10
            value: 0.48123
          - type: ndcg_at_100
            value: 0.56049
          - type: ndcg_at_1000
            value: 0.57979
          - type: ndcg_at_20
            value: 0.51785
          - type: ndcg_at_3
            value: 0.41243
          - type: ndcg_at_5
            value: 0.4386
          - type: recall_at_1
            value: 0.20401
          - type: recall_at_10
            value: 0.58779
          - type: recall_at_100
            value: 0.8584
          - type: recall_at_1000
            value: 0.97364
          - type: recall_at_20
            value: 0.69061
          - type: recall_at_3
            value: 0.36573
          - type: recall_at_5
            value: 0.47495
      - task:
          type: Retrieval
        dataset:
          name: Miracl (es)
          type: miracl/mmteb-miracl
          config: es
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.5571
          - type: ndcg_at_10
            value: 0.49688
          - type: ndcg_at_100
            value: 0.60493
          - type: ndcg_at_1000
            value: 0.62922
          - type: ndcg_at_20
            value: 0.54438
          - type: ndcg_at_3
            value: 0.47981
          - type: ndcg_at_5
            value: 0.46584
          - type: recall_at_1
            value: 0.1638
          - type: recall_at_10
            value: 0.54155
          - type: recall_at_100
            value: 0.85136
          - type: recall_at_1000
            value: 0.96951
          - type: recall_at_20
            value: 0.65329
          - type: recall_at_3
            value: 0.31503
          - type: recall_at_5
            value: 0.40356
      - task:
          type: Retrieval
        dataset:
          name: Miracl (fa)
          type: miracl/mmteb-miracl
          config: fa
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.39873
          - type: ndcg_at_10
            value: 0.50226
          - type: ndcg_at_100
            value: 0.56517
          - type: ndcg_at_1000
            value: 0.57967
          - type: ndcg_at_20
            value: 0.5292
          - type: ndcg_at_3
            value: 0.42738
          - type: ndcg_at_5
            value: 0.45843
          - type: recall_at_1
            value: 0.25369
          - type: recall_at_10
            value: 0.63776
          - type: recall_at_100
            value: 0.87686
          - type: recall_at_1000
            value: 0.9671
          - type: recall_at_20
            value: 0.72099
          - type: recall_at_3
            value: 0.43808
          - type: recall_at_5
            value: 0.52378
      - task:
          type: Retrieval
        dataset:
          name: Miracl (fi)
          type: miracl/mmteb-miracl
          config: fi
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.60818
          - type: ndcg_at_10
            value: 0.6746
          - type: ndcg_at_100
            value: 0.71516
          - type: ndcg_at_1000
            value: 0.7218
          - type: ndcg_at_20
            value: 0.69692
          - type: ndcg_at_3
            value: 0.6006
          - type: ndcg_at_5
            value: 0.63842
          - type: recall_at_1
            value: 0.39264
          - type: recall_at_10
            value: 0.78577
          - type: recall_at_100
            value: 0.93291
          - type: recall_at_1000
            value: 0.97493
          - type: recall_at_20
            value: 0.85435
          - type: recall_at_3
            value: 0.61055
          - type: recall_at_5
            value: 0.69774
      - task:
          type: Retrieval
        dataset:
          name: Miracl (fr)
          type: miracl/mmteb-miracl
          config: fr
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.3965
          - type: ndcg_at_10
            value: 0.49891
          - type: ndcg_at_100
            value: 0.56492
          - type: ndcg_at_1000
            value: 0.57837
          - type: ndcg_at_20
            value: 0.53163
          - type: ndcg_at_3
            value: 0.39843
          - type: ndcg_at_5
            value: 0.44416
          - type: recall_at_1
            value: 0.22644
          - type: recall_at_10
            value: 0.65169
          - type: recall_at_100
            value: 0.89786
          - type: recall_at_1000
            value: 0.98081
          - type: recall_at_20
            value: 0.75338
          - type: recall_at_3
            value: 0.39798
          - type: recall_at_5
            value: 0.51001
      - task:
          type: Retrieval
        dataset:
          name: Miracl (hi)
          type: miracl/mmteb-miracl
          config: hi
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.36857
          - type: ndcg_at_10
            value: 0.46141
          - type: ndcg_at_100
            value: 0.52565
          - type: ndcg_at_1000
            value: 0.54319
          - type: ndcg_at_20
            value: 0.49384
          - type: ndcg_at_3
            value: 0.39469
          - type: ndcg_at_5
            value: 0.4184
          - type: recall_at_1
            value: 0.20185
          - type: recall_at_10
            value: 0.59474
          - type: recall_at_100
            value: 0.83385
          - type: recall_at_1000
            value: 0.94813
          - type: recall_at_20
            value: 0.69437
          - type: recall_at_3
            value: 0.38993
          - type: recall_at_5
            value: 0.47881
      - task:
          type: Retrieval
        dataset:
          name: Miracl (id)
          type: miracl/mmteb-miracl
          config: id
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.46354
          - type: ndcg_at_10
            value: 0.47229
          - type: ndcg_at_100
            value: 0.5525
          - type: ndcg_at_1000
            value: 0.57648
          - type: ndcg_at_20
            value: 0.50606
          - type: ndcg_at_3
            value: 0.42538
          - type: ndcg_at_5
            value: 0.43717
          - type: recall_at_1
            value: 0.20787
          - type: recall_at_10
            value: 0.54771
          - type: recall_at_100
            value: 0.80689
          - type: recall_at_1000
            value: 0.94032
          - type: recall_at_20
            value: 0.63842
          - type: recall_at_3
            value: 0.36229
          - type: recall_at_5
            value: 0.44437
      - task:
          type: Retrieval
        dataset:
          name: Miracl (ja)
          type: miracl/mmteb-miracl
          config: ja
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.56279
          - type: ndcg_at_10
            value: 0.6281
          - type: ndcg_at_100
            value: 0.67757
          - type: ndcg_at_1000
            value: 0.68667
          - type: ndcg_at_20
            value: 0.6521
          - type: ndcg_at_3
            value: 0.56226
          - type: ndcg_at_5
            value: 0.5866
          - type: recall_at_1
            value: 0.36648
          - type: recall_at_10
            value: 0.7496
          - type: recall_at_100
            value: 0.92461
          - type: recall_at_1000
            value: 0.97827
          - type: recall_at_20
            value: 0.82326
          - type: recall_at_3
            value: 0.55845
          - type: recall_at_5
            value: 0.63854
      - task:
          type: Retrieval
        dataset:
          name: Miracl (ko)
          type: miracl/mmteb-miracl
          config: ko
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.52582
          - type: ndcg_at_10
            value: 0.59216
          - type: ndcg_at_100
            value: 0.65093
          - type: ndcg_at_1000
            value: 0.66204
          - type: ndcg_at_20
            value: 0.62427
          - type: ndcg_at_3
            value: 0.5373
          - type: ndcg_at_5
            value: 0.55886
          - type: recall_at_1
            value: 0.30521
          - type: recall_at_10
            value: 0.71159
          - type: recall_at_100
            value: 0.90203
          - type: recall_at_1000
            value: 0.96714
          - type: recall_at_20
            value: 0.80209
          - type: recall_at_3
            value: 0.515
          - type: recall_at_5
            value: 0.6071
      - task:
          type: Retrieval
        dataset:
          name: Miracl (ru)
          type: miracl/mmteb-miracl
          config: ru
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.47524
          - type: ndcg_at_10
            value: 0.52349
          - type: ndcg_at_100
            value: 0.59725
          - type: ndcg_at_1000
            value: 0.61313
          - type: ndcg_at_20
            value: 0.55669
          - type: ndcg_at_3
            value: 0.46812
          - type: ndcg_at_5
            value: 0.48442
          - type: recall_at_1
            value: 0.24337
          - type: recall_at_10
            value: 0.62437
          - type: recall_at_100
            value: 0.86489
          - type: recall_at_1000
            value: 0.95266
          - type: recall_at_20
            value: 0.71411
          - type: recall_at_3
            value: 0.42927
          - type: recall_at_5
            value: 0.51258
      - task:
          type: Retrieval
        dataset:
          name: Miracl (sw)
          type: miracl/mmteb-miracl
          config: sw
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.5166
          - type: ndcg_at_10
            value: 0.61271
          - type: ndcg_at_100
            value: 0.66099
          - type: ndcg_at_1000
            value: 0.66867
          - type: ndcg_at_20
            value: 0.63643
          - type: ndcg_at_3
            value: 0.54828
          - type: ndcg_at_5
            value: 0.57382
          - type: recall_at_1
            value: 0.35277
          - type: recall_at_10
            value: 0.74368
          - type: recall_at_100
            value: 0.92261
          - type: recall_at_1000
            value: 0.97109
          - type: recall_at_20
            value: 0.81888
          - type: recall_at_3
            value: 0.56739
          - type: recall_at_5
            value: 0.6421
      - task:
          type: Retrieval
        dataset:
          name: Miracl (te)
          type: miracl/mmteb-miracl
          config: te
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.63768
          - type: ndcg_at_10
            value: 0.79193
          - type: ndcg_at_100
            value: 0.80243
          - type: ndcg_at_1000
            value: 0.80438
          - type: ndcg_at_20
            value: 0.79549
          - type: ndcg_at_3
            value: 0.76031
          - type: ndcg_at_5
            value: 0.77915
          - type: recall_at_1
            value: 0.63084
          - type: recall_at_10
            value: 0.92411
          - type: recall_at_100
            value: 0.97363
          - type: recall_at_1000
            value: 0.98833
          - type: recall_at_20
            value: 0.9374
          - type: recall_at_3
            value: 0.84159
          - type: recall_at_5
            value: 0.88627
      - task:
          type: Retrieval
        dataset:
          name: Miracl (th)
          type: miracl/mmteb-miracl
          config: th
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.66712
          - type: ndcg_at_10
            value: 0.73324
          - type: ndcg_at_100
            value: 0.76633
          - type: ndcg_at_1000
            value: 0.77119
          - type: ndcg_at_20
            value: 0.75243
          - type: ndcg_at_3
            value: 0.67393
          - type: ndcg_at_5
            value: 0.70201
          - type: recall_at_1
            value: 0.47106
          - type: recall_at_10
            value: 0.84294
          - type: recall_at_100
            value: 0.95949
          - type: recall_at_1000
            value: 0.98874
          - type: recall_at_20
            value: 0.90085
          - type: recall_at_3
            value: 0.68456
          - type: recall_at_5
            value: 0.75915
      - task:
          type: Retrieval
        dataset:
          name: Miracl (yo)
          type: miracl/mmteb-miracl
          config: yo
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.4958
          - type: ndcg_at_10
            value: 0.68705
          - type: ndcg_at_100
            value: 0.70664
          - type: ndcg_at_1000
            value: 0.71197
          - type: ndcg_at_20
            value: 0.698
          - type: ndcg_at_3
            value: 0.64793
          - type: ndcg_at_5
            value: 0.66709
          - type: recall_at_1
            value: 0.46289
          - type: recall_at_10
            value: 0.85154
          - type: recall_at_100
            value: 0.93557
          - type: recall_at_1000
            value: 0.97479
          - type: recall_at_20
            value: 0.89076
          - type: recall_at_3
            value: 0.7507
          - type: recall_at_5
            value: 0.79202
      - task:
          type: Retrieval
        dataset:
          name: Miracl (zh)
          type: miracl/mmteb-miracl
          config: zh
          split: dev
        metrics:
          - type: ndcg_at_1
            value: 0.47583
          - type: ndcg_at_10
            value: 0.52553
          - type: ndcg_at_100
            value: 0.6
          - type: ndcg_at_1000
            value: 0.61415
          - type: ndcg_at_20
            value: 0.55668
          - type: ndcg_at_3
            value: 0.45839
          - type: ndcg_at_5
            value: 0.48127
          - type: recall_at_1
            value: 0.24488
          - type: recall_at_10
            value: 0.63659
          - type: recall_at_100
            value: 0.89702
          - type: recall_at_1000
            value: 0.97996
          - type: recall_at_20
            value: 0.72652
          - type: recall_at_3
            value: 0.42827
          - type: recall_at_5
            value: 0.52081

Llamacpp Static Quantizations of granite-embedding-278m-multilingual

Using llama.cpp release b4381 for quantization.

Original model: https://huggingface.co/ibm-granite/granite-embedding-278m-multilingual

Run them in LM Studio

Prompt format

No prompt format found, check original model page

What's new:

Fix tokenizer

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

Filename Quant type File Size Split Description
granite-embedding-278m-multilingual-f16.gguf f16 0.56GB false Full F16 weights.
granite-embedding-278m-multilingual-Q8_0.gguf Q8_0 0.30GB false Extremely high quality, generally unneeded but max available quant.
granite-embedding-278m-multilingual-Q6_K_L.gguf Q6_K_L 0.28GB false Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
granite-embedding-278m-multilingual-Q5_K_L.gguf Q5_K_L 0.27GB false Uses Q8_0 for embed and output weights. High quality, recommended.
granite-embedding-278m-multilingual-Q4_K_L.gguf Q4_K_L 0.27GB false Uses Q8_0 for embed and output weights. Good quality, recommended.
granite-embedding-278m-multilingual-Q3_K_XL.gguf Q3_K_XL 0.26GB false Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
granite-embedding-278m-multilingual-Q6_K.gguf Q6_K 0.24GB false Very high quality, near perfect, recommended.
granite-embedding-278m-multilingual-Q5_K_M.gguf Q5_K_M 0.23GB false High quality, recommended.
granite-embedding-278m-multilingual-Q5_K_S.gguf Q5_K_S 0.22GB false High quality, recommended.
granite-embedding-278m-multilingual-Q4_K_M.gguf Q4_K_M 0.22GB false Good quality, default size for most use cases, recommended.
granite-embedding-278m-multilingual-Q4_K_S.gguf Q4_K_S 0.21GB false Slightly lower quality with more space savings, recommended.
granite-embedding-278m-multilingual-Q4_0.gguf Q4_0 0.21GB false Legacy format, offers online repacking for ARM and AVX CPU inference.
granite-embedding-278m-multilingual-IQ4_NL.gguf IQ4_NL 0.21GB false Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference.
granite-embedding-278m-multilingual-IQ4_XS.gguf IQ4_XS 0.21GB false Decent quality, smaller than Q4_K_S with similar performance, recommended.
granite-embedding-278m-multilingual-Q3_K_L.gguf Q3_K_L 0.21GB false Lower quality but usable, good for low RAM availability.
granite-embedding-278m-multilingual-Q3_K_M.gguf Q3_K_M 0.21GB false Low quality.
granite-embedding-278m-multilingual-IQ3_M.gguf IQ3_M 0.21GB false Medium-low quality, new method with decent performance comparable to Q3_K_M.

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/granite-embedding-278m-multilingual-GGUF --include "granite-embedding-278m-multilingual-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/granite-embedding-278m-multilingual-GGUF --include "granite-embedding-278m-multilingual-Q8_0/*" --local-dir ./

You can either specify a new local-dir (granite-embedding-278m-multilingual-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)
model size params backend threads test t/s % (vs Q4_0)
qwen2 3B Q4_0 1.70 GiB 3.09 B CPU 64 pp512 204.03 ± 1.03 100%
qwen2 3B Q4_0 1.70 GiB 3.09 B CPU 64 pp1024 282.92 ± 0.19 100%
qwen2 3B Q4_0 1.70 GiB 3.09 B CPU 64 pp2048 259.49 ± 0.44 100%
qwen2 3B Q4_0 1.70 GiB 3.09 B CPU 64 tg128 39.12 ± 0.27 100%
qwen2 3B Q4_0 1.70 GiB 3.09 B CPU 64 tg256 39.31 ± 0.69 100%
qwen2 3B Q4_0 1.70 GiB 3.09 B CPU 64 tg512 40.52 ± 0.03 100%
qwen2 3B Q4_K_M 1.79 GiB 3.09 B CPU 64 pp512 301.02 ± 1.74 147%
qwen2 3B Q4_K_M 1.79 GiB 3.09 B CPU 64 pp1024 287.23 ± 0.20 101%
qwen2 3B Q4_K_M 1.79 GiB 3.09 B CPU 64 pp2048 262.77 ± 1.81 101%
qwen2 3B Q4_K_M 1.79 GiB 3.09 B CPU 64 tg128 18.80 ± 0.99 48%
qwen2 3B Q4_K_M 1.79 GiB 3.09 B CPU 64 tg256 24.46 ± 3.04 83%
qwen2 3B Q4_K_M 1.79 GiB 3.09 B CPU 64 tg512 36.32 ± 3.59 90%
qwen2 3B Q4_0_8_8 1.69 GiB 3.09 B CPU 64 pp512 271.71 ± 3.53 133%
qwen2 3B Q4_0_8_8 1.69 GiB 3.09 B CPU 64 pp1024 279.86 ± 45.63 100%
qwen2 3B Q4_0_8_8 1.69 GiB 3.09 B CPU 64 pp2048 320.77 ± 5.00 124%
qwen2 3B Q4_0_8_8 1.69 GiB 3.09 B CPU 64 tg128 43.51 ± 0.05 111%
qwen2 3B Q4_0_8_8 1.69 GiB 3.09 B CPU 64 tg256 43.35 ± 0.09 110%
qwen2 3B Q4_0_8_8 1.69 GiB 3.09 B CPU 64 tg512 42.60 ± 0.31 105%

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 and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

The I-quants are not compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.

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.

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