---
base_model: rinna/youri-7b
datasets:
- mc4
- wikipedia
- EleutherAI/pile
- oscar-corpus/colossal-oscar-1.0
- cc100
language:
- ja
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
---
## About

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weighted/imatrix quants of https://huggingface.co/rinna/youri-7b

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static quants are available at https://huggingface.co/mradermacher/youri-7b-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/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ1_S.gguf) | i1-IQ1_S | 1.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ1_M.gguf) | i1-IQ1_M | 1.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.0 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.1 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ2_S.gguf) | i1-IQ2_S | 2.3 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ2_M.gguf) | i1-IQ2_M | 2.5 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q2_K.gguf) | i1-Q2_K | 2.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 2.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 2.9 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ3_S.gguf) | i1-IQ3_S | 3.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ3_M.gguf) | i1-IQ3_M | 3.2 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 3.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 3.7 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q4_0.gguf) | i1-Q4_0 | 3.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 4.8 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 4.9 |  |
| [GGUF](https://huggingface.co/mradermacher/youri-7b-i1-GGUF/resolve/main/youri-7b.i1-Q6_K.gguf) | i1-Q6_K | 5.6 | practically like static Q6_K |

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 hardware for calculating the imatrix for these quants.

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