README.md

---
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  ### DBRX Terms of Use
  Use of DBRX is governed by the [Databricks Open Model License](https://www.databricks.com/legal/open-model-license) and the [Databricks Open Model Acceptable Use Policy](https://www.databricks.com/legal/acceptable-use-policy-open-model).
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About

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weighted/imatrix quants of https://huggingface.co/alpindale/dbrx-instruct

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static quants are available at https://huggingface.co/mradermacher/dbrx-instruct-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/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 43.3 |  |
| [GGUF](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q2_K.gguf) | i1-Q2_K | 48.0 | IQ3_XXS probably better |
| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 50.8 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 63.2 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 75.0 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 80.0 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/dbrx-instruct-i1-GGUF/resolve/main/dbrx-instruct.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 108.1 | 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.

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