base_model: bigscience/bloomz-mt
datasets:
- bigscience/xP3mt
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zu
library_name: transformers
license: bigscience-bloom-rail-1.0
quantized_by: mradermacher
About
static quants of https://huggingface.co/bigscience/bloomz-mt
weighted/imatrix quants are available at https://huggingface.co/mradermacher/bloomz-mt-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 |
|---|---|---|---|
| PART 1 PART 2 | Q2_K | 68.2 | |
| PART 1 PART 2 | Q3_K_S | 78.8 | |
| PART 1 PART 2 | Q3_K_M | 94.5 | lower quality |
| PART 1 PART 2 | IQ4_XS | 97.8 | |
| PART 1 PART 2 PART 3 | Q4_K_S | 103.1 | fast, recommended |
| PART 1 PART 2 PART 3 | Q3_K_L | 103.1 | |
| PART 1 PART 2 PART 3 | Q4_K_M | 114.8 | fast, recommended |
| PART 1 PART 2 PART 3 | Q5_K_S | 124.3 | |
| PART 1 PART 2 PART 3 | Q5_K_M | 133.7 | |
| PART 1 PART 2 PART 3 | Q6_K | 147.7 | very good quality |
| PART 1 PART 2 PART 3 PART 4 | Q8_0 | 191.2 | 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. 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.