datasets:
- ehartford/based
exported_from: cognitivecomputations/based-30b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
About
weighted/imatrix quants of https://huggingface.co/cognitivecomputations/based-30b
static quants are available at https://huggingface.co/mradermacher/based-30b-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 | i1-IQ2_M | 11.3 | |
| GGUF | i1-Q2_K | 12.1 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 12.4 | lower quality |
| GGUF | i1-Q3_K_S | 14.2 | IQ3_XS probably better |
| GGUF | i1-Q3_K_M | 15.9 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| GGUF | i1-IQ4_XS | 17.4 | |
| GGUF | i1-Q4_0 | 18.5 | fast, low quality |
| GGUF | i1-Q4_K_S | 18.6 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 19.7 | fast, recommended |
| GGUF | i1-Q5_K_S | 22.5 | |
| GGUF | i1-Q5_K_M | 23.1 | |
| GGUF | i1-Q6_K | 26.8 | 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, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.