---
base_model: karakuri-ai/karakuri-lm-8x7b-instruct-v0.1
datasets:
- databricks/databricks-dolly-15k
- glaiveai/glaive-code-assistant-v3
- glaiveai/glaive-function-calling-v2
- gretelai/synthetic_text_to_sql
- meta-math/MetaMathQA
- microsoft/orca-math-word-problems-200k
- neural-bridge/rag-dataset-12000
- neural-bridge/rag-hallucination-dataset-1000
- nvidia/HelpSteer
- OpenAssistant/oasst2
language:
- en
- ja
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mixtral
- steerlm
---
## About

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static quants of https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-instruct-v0.1

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weighted/imatrix quants are available at https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-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/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q2_K.gguf) | Q2_K | 17.4 |  |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q3_K_S.gguf) | Q3_K_S | 20.5 |  |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q3_K_M.gguf) | Q3_K_M | 22.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q3_K_L.gguf) | Q3_K_L | 24.3 |  |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.IQ4_XS.gguf) | IQ4_XS | 25.5 |  |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q4_K_S.gguf) | Q4_K_S | 26.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q5_K_S.gguf) | Q5_K_S | 32.3 |  |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q5_K_M.gguf) | Q5_K_M | 33.3 |  |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q6_K.gguf) | Q6_K | 38.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/karakuri-lm-8x7b-instruct-v0.1-GGUF/resolve/main/karakuri-lm-8x7b-instruct-v0.1.Q8_0.gguf) | Q8_0 | 49.7 | fast, best 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

## 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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