|
--- |
|
base_model: |
|
- CultriX/MonaTrix-v4 |
|
- mlabonne/OmniTruthyBeagle-7B-v0 |
|
- CultriX/MoNeuTrix-7B-v1 |
|
- paulml/OmniBeagleSquaredMBX-v3-7B |
|
exported_from: CultriX/NeuralMona_MoE-4x7B |
|
language: |
|
- en |
|
library_name: transformers |
|
license: apache-2.0 |
|
quantized_by: mradermacher |
|
tags: |
|
- moe |
|
- frankenmoe |
|
- merge |
|
- mergekit |
|
- lazymergekit |
|
- CultriX/MonaTrix-v4 |
|
- mlabonne/OmniTruthyBeagle-7B-v0 |
|
- CultriX/MoNeuTrix-7B-v1 |
|
- paulml/OmniBeagleSquaredMBX-v3-7B |
|
--- |
|
## About |
|
|
|
weighted/imatrix quants of https://huggingface.co/CultriX/NeuralMona_MoE-4x7B |
|
|
|
<!-- provided-files --> |
|
static quants are available at https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-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/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-IQ2_M.gguf) | i1-IQ2_M | 8.3 | | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q2_K.gguf) | i1-Q2_K | 9.1 | IQ3_XXS probably better | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 9.6 | lower quality | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 10.1 | | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 10.7 | IQ3_XS probably better | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 11.8 | IQ3_S probably better | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 12.8 | IQ3_M probably better | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 13.1 | | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q4_0.gguf) | i1-Q4_0 | 13.9 | | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 14.0 | optimal size/speed/quality | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 14.9 | fast, recommended | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 16.9 | | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 17.4 | | |
|
| [GGUF](https://huggingface.co/mradermacher/NeuralMona_MoE-4x7B-i1-GGUF/resolve/main/NeuralMona_MoE-4x7B.i1-Q6_K.gguf) | i1-Q6_K | 20.1 | practically like static Q6_K | |
|
|
|
|
|
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 |
|
|
|
## 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. |
|
|
|
<!-- end --> |
|
|
