base_model: QuixiAI/Wizard-Vicuna-7B-Uncensored
datasets:
- ehartford/wizard_vicuna_70k_unfiltered
language: - en
library_name: transformers
license: other
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags: - uncensored
About
static quants of https://huggingface.co/QuixiAI/Wizard-Vicuna-7B-Uncensored
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Wizard-Vicuna-7B-Uncensored-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 | 2.6 | |
| GGUF | Q3_K_S | 3.0 | |
| GGUF | Q3_K_M | 3.4 | lower quality |
| GGUF | Q3_K_L | 3.7 | |
| GGUF | IQ4_XS | 3.7 | |
| GGUF | Q4_0_4_4 | 3.9 | fast on arm, low quality |
| GGUF | Q4_K_S | 4.0 | fast, recommended |
| GGUF | Q4_K_M | 4.2 | fast, recommended |
| GGUF | Q5_K_S | 4.8 | |
| GGUF | Q5_K_M | 4.9 | |
| GGUF | Q6_K | 5.6 | very good quality |
| GGUF | Q8_0 | 7.3 | fast, best quality |
| GGUF | f16 | 13.6 | 16 bpw, overkill |
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. Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.