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---
base_model: monsterbeasts/LishizhenGPT
datasets:
- bigscience/xP3mt
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
library_name: transformers
license: bigscience-bloom-rail-1.0
quantized_by: mradermacher
---
## About
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static quants of https://huggingface.co/monsterbeasts/LishizhenGPT
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weighted/imatrix quants are available at https://huggingface.co/mradermacher/LishizhenGPT-i1-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/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q2_K.gguf) | Q2_K | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q3_K_S.gguf) | Q3_K_S | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q3_K_M.gguf) | Q3_K_M | 4.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q4_0_4_4.gguf) | Q4_0_4_4 | 4.9 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q4_K_S.gguf) | Q4_K_S | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q5_K_M.gguf) | Q5_K_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.Q8_0.gguf) | Q8_0 | 8.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LishizhenGPT-GGUF/resolve/main/LishizhenGPT.f16.gguf) | f16 | 16.3 | 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. 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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