---
base_model: cognitivecomputations/dolphin-2.9-mixtral-8x22b
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- HuggingFaceH4/ultrachat_200k
- microsoft/orca-math-word-problems-200k
- abacusai/SystemChat-1.1
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- generated_from_trainer
- axolotl
---
## About

<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type:  -->
weighted/imatrix quants of https://huggingface.co/cognitivecomputations/dolphin-2.9-mixtral-8x22b

<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-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/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 38.0 |  |
| [GGUF](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ2_M.gguf) | i1-IQ2_M | 46.8 |  |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 52.2 | IQ3_XXS probably better |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 55.0 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 58.3 |  |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 61.6 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 61.6 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 64.6 |  |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 67.9 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 72.7 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 75.6 |  |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 80.0 | fast, low quality |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 80.6 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 85.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 97.1 |  |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q5_K_M.gguf.part3of3) | i1-Q5_K_M | 100.1 |  |
| [PART 1](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-i1-GGUF/resolve/main/dolphin-2.9-mixtral-8x22b.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 115.6 | 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.

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