192 lines
6.2 KiB
Markdown
192 lines
6.2 KiB
Markdown
---
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base_model: mixedbread-ai/mxbai-rerank-base-v2
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language:
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- af
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- am
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- ar
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- as
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- az
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- be
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- bg
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- bn
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- br
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- bs
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- ca
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- cs
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- cy
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- da
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- de
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- el
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- en
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- eo
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- es
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- et
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- eu
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- fa
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- ff
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- fi
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- fr
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- fy
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- ga
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- gd
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- gl
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- gn
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- gu
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- ha
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- he
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- hi
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- hr
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- ht
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- hu
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- hy
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- id
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- ig
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- is
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- it
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- ja
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- jv
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- ka
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- kk
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- km
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- kn
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- ko
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- ku
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- ky
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- la
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- lg
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- li
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- ln
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- lo
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- lt
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- lv
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- mg
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- mk
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- ml
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- mn
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- mr
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- ms
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- my
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- ne
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- nl
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- no
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- ns
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- om
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- or
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- pa
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- pt
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- qu
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- rm
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- ro
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- ru
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- sa
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- sc
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- sd
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- si
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- sk
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- sl
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- so
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- sq
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- sr
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- ss
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- su
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- sv
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- sw
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- ta
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- te
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- th
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- tl
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- tn
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- tr
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- ug
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- uk
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- ur
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- uz
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- vi
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- wo
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- xh
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- yi
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- yo
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- zh
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- zu
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library_name: transformers
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license: apache-2.0
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mradermacher:
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readme_rev: 1
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quantized_by: mradermacher
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tags:
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- sentence-transformers
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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2
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<!-- provided-files -->
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***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#mxbai-rerank-base-v2-i1-GGUF).***
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static quants are available at https://huggingface.co/mradermacher/mxbai-rerank-base-v2-GGUF
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.4 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.4 | IQ3_XS probably better |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ3_S.gguf) | i1-IQ3_S | 0.4 | beats Q3_K* |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.4 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ3_M.gguf) | i1-IQ3_M | 0.4 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.4 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.5 | prefer IQ4_XS |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q4_0.gguf) | i1-Q4_0 | 0.5 | fast, low quality |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.5 | IQ3_S probably better |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.5 | IQ3_M probably better |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q4_1.gguf) | i1-Q4_1 | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.5 | optimal size/speed/quality |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.5 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/mxbai-rerank-base-v2-i1-GGUF/resolve/main/mxbai-rerank-base-v2.i1-Q6_K.gguf) | i1-Q6_K | 0.6 | practically like static Q6_K |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
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