Downloads · 30 days
47
45% of all-time downloads
mradermacher/orthorus_125b_v3-GGUF
orthorus_125b_v3-GGUF is a machine learning model from mradermacher. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
static quants of https://huggingface.co/ibivibiv/orthorus125bv3
Downloads · 30 days
47
45% of all-time downloads
All-time downloads
105
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.part1of2291 GB · 34%
From the Hugging Face model README
static quants of https://huggingface.co/ibivibiv/orthorus_125b_v3
<!-- provided-files -->For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/orthorus_125b_v3-i1-GGUF
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.
(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 | 46.0 | |
| PART 1 PART 2 | Q3_K_S | 54.2 | |
| PART 1 PART 2 | Q3_K_M | 60.2 | lower quality |
| PART 1 PART 2 | Q3_K_L | 65.3 | |
| PART 1 PART 2 | IQ4_XS | 67.6 | |
| PART 1 PART 2 | Q4_K_S | 71.4 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 75.7 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 86.3 | |
| PART 1 PART 2 | Q5_K_M | 88.9 | |
| PART 1 PART 2 PART 3 | Q6_K | 102.9 | very good quality |
| PART 1 PART 2 PART 3 | Q8_0 | 133.3 | 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
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
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.
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