datasets:
- Open-Orca/OpenOrca
- openchat/openchat_sharegpt4_dataset
- LDJnr/Puffin
- ehartford/samantha-data
- OpenAssistant/oasst1
- jondurbin/airoboros-gpt4-1.4.1
exported_from: ICBU-NPU/FashionGPT-70B-V1.1
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
About
static quants of https://huggingface.co/ICBU-NPU/FashionGPT-70B-V1.1
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
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 | 25.6 | |
| GGUF | IQ3_XS | 28.4 | |
| GGUF | IQ3_S | 30.0 | beats Q3_K* |
| GGUF | Q3_K_S | 30.0 | |
| GGUF | IQ3_M | 31.0 | |
| GGUF | Q3_K_M | 33.4 | lower quality |
| GGUF | Q3_K_L | 36.2 | |
| GGUF | IQ4_XS | 37.3 | |
| GGUF | Q4_K_S | 39.3 | fast, recommended |
| GGUF | Q4_K_M | 41.5 | fast, recommended |
| GGUF | Q5_K_S | 47.6 | |
| GGUF | Q5_K_M | 48.9 | |
| PART 1 PART 2 | Q6_K | 56.7 | very good quality |
| PART 1 PART 2 | Q8_0 | 73.4 | 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.