base_model: Downtown-Case/Qwen2.5-32B-EVA-Instruct-Merge-0.1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
About
weighted/imatrix quants of https://huggingface.co/Downtown-Case/Qwen2.5-32B-EVA-Instruct-Merge-0.1
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-32B-EVA-Instruct-Merge-0.1-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-IQ1_M | 8.0 | mostly desperate |
| GGUF | i1-IQ2_M | 11.4 | |
| GGUF | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 12.9 | lower quality |
| GGUF | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| GGUF | i1-IQ3_M | 14.9 | |
| GGUF | i1-Q3_K_M | 16.0 | IQ3_S probably better |
| GGUF | i1-IQ4_XS | 17.8 | |
| GGUF | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 20.0 | fast, recommended |
| GGUF | i1-Q6_K | 27.0 | 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
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.