Downloads · 30 days
178
13% of all-time downloads
MiniMax-M2-THRIFT-55-GGUF
mradermacher/MiniMax-M2-THRIFT-55-GGUF
MiniMax-M2-THRIFT-55-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/lemuralabs/MiniMax-M2-Pruned-55
All-time downloads
1.3K
Public
Repo size
719 GB
Likes
2
Public
Repo makeup
Click a slice to open those files.
.part1of2264 GB · 37%
At a glance
- Library
- transformers
- License
- mit
- Access
- Public
- Created
- Nov 26, 2025
- Updated
- Aug 17, 2026
- SHA
- 4cdae67d
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/lemuralabs/MiniMax-M2-Pruned-55
<!-- 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/MiniMax-M2-THRIFT-55-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 | 38.8 | |
| GGUF | Q3_K_S | 45.9 | |
| PART 1 PART 2 | Q3_K_M | 50.8 | lower quality |
| PART 1 PART 2 | Q3_K_L | 55.0 | |
| PART 1 PART 2 | IQ4_XS | 57.2 | |
| PART 1 PART 2 | Q4_K_S | 60.3 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 64.1 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 73.0 | |
| PART 1 PART 2 | Q5_K_M | 75.2 | |
| PART 1 PART 2 | Q6_K | 87.0 | very good quality |
| PART 1 PART 2 PART 3 | Q8_0 | 112.6 | 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
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.
<!-- end -->- Library
- transformers
- License
- mit
- Languages
- en
- Created
- Nov 26, 2025
- Updated
- Aug 17, 2026