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---
base_model: MaziyarPanahi/Mistral-7B-Instruct-Aya-101
datasets:
- CohereForAI/aya_dataset
language:
- afr
- amh
- ara
- aze
- bel
- ben
- bul
- cat
- ceb
- ces
- cym
- dan
- deu
- ell
- eng
- epo
- est
- eus
- fin
- fil
- fra
- fry
- gla
- gle
- glg
- guj
- hat
- hau
- heb
- hin
- hun
- hye
- ibo
- ind
- isl
- ita
- jav
- jpn
- kan
- kat
- kaz
- khm
- kir
- kor
- kur
- lao
- lav
- lat
- lit
- ltz
- mal
- mar
- mkd
- mlg
- mlt
- mon
- mri
- msa
- mya
- nep
- nld
- nor
- nso
- nya
- ory
- pan
- pes
- pol
- por
- pus
- ron
- rus
- sin
- slk
- slv
- smo
- sna
- snd
- som
- sot
- spa
- sqi
- srp
- sun
- swa
- swe
- tam
- tel
- tgk
- tha
- tur
- twi
- ukr
- urd
- uzb
- vie
- xho
- yid
- yor
- zho
- zul
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- axolotl
- mistral
- 7b
- generated_from_trainer
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
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<!-- ### vocab_type: -->
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static quants of https://huggingface.co/MaziyarPanahi/Mistral-7B-Instruct-Aya-101
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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/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.2 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7B-Instruct-Aya-101-GGUF/resolve/main/Mistral-7B-Instruct-Aya-101.f16.gguf) | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
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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