base_model: INSAIT-Institute/MamayLM-Gemma-3-4B-IT-v1.0
datasets:
- Goader/kobza
- HuggingFaceFW/fineweb-2
- HPLT/HPLT2.0_cleaned
- wikimedia/wikipedia
- HuggingFaceTB/smoltalk2
- open-r1/Mixture-of-Thoughts
language:
- uk
- en
library_name: transformers
license: gemma
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- gemma3
- instruct
- mamaylm
- insait
About
static quants of https://huggingface.co/INSAIT-Institute/MamayLM-Gemma-3-4B-IT-v1.0
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/MamayLM-Gemma-3-4B-IT-v1.0-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 | 0.7 | multi-modal supplement |
| GGUF | mmproj-f16 | 1.0 | multi-modal supplement |
| GGUF | Q2_K | 1.8 | |
| GGUF | Q3_K_S | 2.0 | |
| GGUF | Q3_K_M | 2.2 | lower quality |
| GGUF | Q3_K_L | 2.3 | |
| GGUF | IQ4_XS | 2.4 | |
| GGUF | Q4_K_S | 2.5 | fast, recommended |
| GGUF | Q4_K_M | 2.6 | fast, recommended |
| GGUF | Q5_K_S | 2.9 | |
| GGUF | Q5_K_M | 2.9 | |
| GGUF | Q6_K | 3.3 | very good quality |
| GGUF | Q8_0 | 4.2 | fast, best quality |
| GGUF | f16 | 7.9 | 16 bpw, overkill |
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.