README.md

---
base_model: lightblue/lb-reranker-0.5B-v1.0
datasets:
- lightblue/reranker_continuous_filt_max7_train
language:
- en
- zh
- es
- de
- ar
- ru
- ja
- ko
- hi
- sk
- vi
- tr
- fi
- id
- fa
- no
- th
- sv
- pt
- da
- bn
- te
- ro
- it
- fr
- nl
- sw
- pl
- hu
- cs
- el
- uk
- mr
- ta
- tl
- bg
- lt
- ur
- he
- gu
- kn
- am
- kk
- hr
- uz
- jv
- ca
- az
- ms
- sr
- sl
- yo
- lv
- is
- ha
- ka
- et
- bs
- hy
- ml
- pa
- mt
- km
- sq
- or
- as
- my
- mn
- af
- be
- ga
- mk
- cy
- gl
- ceb
- la
- yi
- lb
- tg
- gd
- ne
- ps
- eu
- ky
- ku
- si
- ht
- eo
- lo
- fy
- sd
- mg
- so
- ckb
- su
- nn
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- reranker
---
## About

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static quants of https://huggingface.co/lightblue/lb-reranker-0.5B-v1.0

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weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.

## Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q3_K_S.gguf) | Q3_K_S | 0.4 |  |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q2_K.gguf) | Q2_K | 0.4 |  |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.IQ4_XS.gguf) | IQ4_XS | 0.5 |  |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q3_K_L.gguf) | Q3_K_L | 0.5 |  |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q5_K_S.gguf) | Q5_K_S | 0.5 |  |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q5_K_M.gguf) | Q5_K_M | 0.5 |  |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/lb-reranker-0.5B-v1.0-GGUF/resolve/main/lb-reranker-0.5B-v1.0.f16.gguf) | f16 | 1.1 | 16 bpw, overkill |

Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

## FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

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