---
base_model: RichardErkhov/FATLLAMA-1.7T-Instruct
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About

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static quants of https://huggingface.co/RichardErkhov/FATLLAMA-1.7T-Instruct

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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 |
|:-----|:-----|--------:|:------|
| [P1](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part01of20) [P2](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part02of20) [P3](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part03of20) [P4](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part04of20) [P5](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part05of20) [P6](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part06of20) [P7](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part07of20) [P8](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part08of20) [P9](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part09of20) [P10](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part10of20) [P11](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part11of20) [P12](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part12of20) [P13](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part13of20) [P14](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part14of20) [P15](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part15of20) [P16](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part16of20) [P17](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part17of20) [P18](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part18of20) [P19](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part19of20) [P20](https://huggingface.co/mradermacher/FATLLAMA-1.7T-Instruct-GGUF/resolve/main/FATLLAMA-1.7T-Instruct.Q4_K_S.gguf.part20of20) | Q4_K_S | 961.5 | fast, recommended |

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. 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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