---
base_model: LLaMAX/LLaMAX2-7B
language:
- af
- am
- ar
- hy
- as
- ast
- az
- be
- bn
- bs
- bg
- my
- ca
- ceb
- zho
- hr
- cs
- da
- nl
- en
- et
- tl
- fi
- fr
- ff
- gl
- lg
- ka
- de
- el
- gu
- ha
- he
- hi
- hu
- is
- ig
- id
- ga
- it
- ja
- jv
- kea
- kam
- kn
- kk
- km
- ko
- ky
- lo
- lv
- ln
- lt
- luo
- lb
- mk
- ms
- ml
- mt
- mi
- mr
- mn
- ne
- ns
- no
- ny
- oc
- or
- om
- ps
- fa
- pl
- pt
- pa
- ro
- ru
- sr
- sn
- sd
- sk
- sl
- so
- ku
- es
- sw
- sv
- tg
- ta
- te
- th
- tr
- uk
- umb
- ur
- uz
- vi
- cy
- wo
- xh
- yo
- zu
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- Multilingual
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
<!-- ### tags:  -->
static quants of https://huggingface.co/LLaMAX/LLaMAX2-7B

<!-- provided-files -->
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/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q2_K.gguf) | Q2_K | 2.6 |  |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.IQ3_XS.gguf) | IQ3_XS | 2.9 |  |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.IQ3_S.gguf) | IQ3_S | 3.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q3_K_S.gguf) | Q3_K_S | 3.0 |  |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.IQ3_M.gguf) | IQ3_M | 3.2 |  |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q3_K_L.gguf) | Q3_K_L | 3.7 |  |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.IQ4_XS.gguf) | IQ4_XS | 3.7 |  |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q5_K_S.gguf) | Q5_K_S | 4.8 |  |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q5_K_M.gguf) | Q5_K_M | 4.9 |  |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q6_K.gguf) | Q6_K | 5.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMAX2-7B-GGUF/resolve/main/LLaMAX2-7B.f16.gguf) | f16 | 13.6 | 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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