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---
datasets:
- Open-Orca/OpenOrca
- openchat/openchat_sharegpt4_dataset
- LDJnr/Puffin
- ehartford/samantha-data
- OpenAssistant/oasst1
- jondurbin/airoboros-gpt4-1.4.1
exported_from: ICBU-NPU/FashionGPT-70B-V1.1
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
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weighted/imatrix quants of https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1
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static quants are available at https://huggingface.co/mradermacher/FashionGPT-70B-V1.1-GGUF
## 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/FashionGPT-70B-V1.1-i1-GGUF/resolve/main/FashionGPT-70B-V1.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
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
## 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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