base_model: Panchovix/Wizard-Vicuna-30B-Uncensored-lxctx-PI-16384-LoRA-fp16
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
About
static quants of https://huggingface.co/Panchovix/Wizard-Vicuna-30B-Uncensored-lxctx-PI-16384-LoRA-fp16
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Wizard-Vicuna-30B-Uncensored-lxctx-PI-16384-LoRA-fp16-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 | 12.1 | |
| GGUF | IQ3_XS | 13.4 | |
| GGUF | IQ3_S | 14.2 | beats Q3_K* |
| GGUF | Q3_K_S | 14.2 | |
| GGUF | IQ3_M | 15.0 | |
| GGUF | Q3_K_M | 15.9 | lower quality |
| GGUF | Q3_K_L | 17.4 | |
| GGUF | IQ4_XS | 17.6 | |
| GGUF | Q4_K_S | 18.6 | fast, recommended |
| GGUF | Q4_K_M | 19.7 | fast, recommended |
| GGUF | Q5_K_S | 22.5 | |
| GGUF | Q5_K_M | 23.1 | |
| GGUF | Q6_K | 26.8 | very good quality |
| GGUF | Q8_0 | 34.7 | 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.