base_model, datasets, language, library_name, license, mradermacher, quantized_by, tags

base_model	datasets	language	library_name	license	mradermacher	quantized_by	tags
EphAsad/Atem-4B	mitroitskii/OpenR1-Math-220k-formatted Jackrong/Claude-opus-4.6-TraceInversion-9000x Jackrong/Kimi-K2.5-Reasoning-1M-Cleaned WithinUsAI/MiniMax_M2.7_Distilled_5k FreedomIntelligence/medical-o1-reasoning-SFT Modotte/CodeX-2M-Thinking trjxter/DeepSeek-V4-Pro-Reasoning-8000x nvidia/OpenCodeReasoning	en	transformers	apache-2.0	readme_rev 1	mradermacher	unsloth lora qwen3 reasoning distillation chain-of-thought

About

static quants of https://huggingface.co/EphAsad/Atem-4B

For a convenient overview and download list, visit our model page for this model.

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Atem-4B-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	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	3.0
GGUF	Q6_K	3.4	very good quality
GGUF	Q8_0	4.4	fast, best quality
GGUF	f16	8.2	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.