base_model:
- tokyotech-llm/Swallow-70b-instruct-hf
- nitky/Swallow-70b-NVE-RP
exported_from: nitky/Swallow-70b-RP
language:
- en
library_name: transformers
license: llama2
model_type: llama
quantized_by: mradermacher
tags:
- mergekit
- merge
About
static quants of https://huggingface.co/nitky/Swallow-70b-RP
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Swallow-70b-RP-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 | 25.7 | |
| GGUF | IQ3_XS | 28.5 | |
| GGUF | IQ3_S | 30.1 | beats Q3_K* |
| GGUF | Q3_K_S | 30.1 | |
| GGUF | IQ3_M | 31.2 | |
| GGUF | Q3_K_M | 33.5 | lower quality |
| GGUF | Q3_K_L | 36.4 | |
| GGUF | IQ4_XS | 37.4 | |
| GGUF | Q4_K_S | 39.5 | fast, recommended |
| GGUF | Q4_K_M | 41.6 | fast, recommended |
| GGUF | Q5_K_S | 47.7 | |
| GGUF | Q5_K_M | 49.0 | |
| PART 1 PART 2 | Q6_K | 56.8 | very good quality |
| PART 1 PART 2 | Q8_0 | 73.6 | 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
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.