base_model: pbevan11/Mistral-Nemo-Instruct-MCAI-SFT-DPO-revision-only
datasets:
- pbevan11/multilingual-constitutional-preference-pairs-revision-only
- pbevan11/ultrafeedback_binarized_multilingual
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
About
static quants of https://huggingface.co/pbevan11/Mistral-Nemo-Instruct-MCAI-SFT-DPO-revision-only
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mistral-Nemo-Instruct-MCAI-SFT-DPO-revision-only-i1-GGUF
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs 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 | Q2_K | 4.9 | |
| GGUF | IQ3_XS | 5.4 | |
| GGUF | Q3_K_S | 5.6 | |
| GGUF | IQ3_S | 5.7 | beats Q3_K* |
| GGUF | IQ3_M | 5.8 | |
| GGUF | Q3_K_M | 6.2 | lower quality |
| GGUF | Q3_K_L | 6.7 | |
| GGUF | IQ4_XS | 6.9 | |
| GGUF | Q4_K_S | 7.2 | fast, recommended |
| GGUF | Q4_K_M | 7.6 | fast, recommended |
| GGUF | Q5_K_S | 8.6 | |
| GGUF | Q5_K_M | 8.8 | |
| GGUF | Q6_K | 10.2 | very good quality |
| GGUF | Q8_0 | 13.1 | fast, best quality |
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
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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.