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MiMo-V2.5-Base-GGUF
mradermacher/MiMo-V2.5-Base-GGUF
MiMo-V2.5-Base-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 mit.
static quants of https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base
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.part1of4212 GB · 10%
At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- May 19, 2026
- Updated
- Oct 9, 2026
- SHA
- 8b4637f0
Base models
Model card
From the Hugging Face model README
About
<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> <!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS --> <!-- ### quants_skip: --> <!-- ### skip_mmproj: -->static quants of https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Base
<!-- 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/MiMo-V2.5-Base-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 | mmproj-Q8_0 | 0.9 | multi-modal supplement |
| GGUF | mmproj-f16 | 1.6 | multi-modal supplement |
| PART 1 PART 2 PART 3 | Q2_K | 113.4 | |
| PART 1 PART 2 PART 3 | Q3_K_S | 133.6 | |
| PART 1 PART 2 PART 3 PART 4 | Q3_K_M | 148.3 | lower quality |
| PART 1 PART 2 PART 3 PART 4 | Q3_K_L | 160.9 | |
| PART 1 PART 2 PART 3 PART 4 | IQ4_XS | 167.2 | |
| PART 1 PART 2 PART 3 PART 4 | Q4_K_S | 176.1 | fast, recommended |
| PART 1 PART 2 PART 3 PART 4 | Q4_K_M | 186.8 | fast, recommended |
| P1 P2 P3 P4 P5 | Q5_K_S | 213.3 | |
| P1 P2 P3 P4 P5 | Q5_K_M | 219.5 | |
| P1 P2 P3 P4 P5 P6 | Q6_K | 254.4 | very good quality |
| P1 P2 P3 P4 P5 P6 P7 | Q8_0 | 329.4 | 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
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.
<!-- end -->- Library
- transformers
- License
- mit
- Languages
- en, zh
- Created
- May 19, 2026
- Updated
- Oct 9, 2026