mradermacher/Atem-8B-GGUF overview

---

base_model: EphAsad/Atem-8B

datasets:

• 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

language:

• en

library_name: transformers

license: apache-2.0

mradermacher:

readme_rev: 1

quantized_by: mradermacher

tags:

• unsloth
• lora
• qwen3
• reasoning
• distillation
• chain-of-thought

---

About

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static quants of https://huggingface.co/EphAsad/Atem-8B

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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-8B-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's

READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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 | 3.4 | |

| GGUF | Q3_K_S | 3.9 | |

| GGUF | Q3_K_M | 4.2 | lower quality |

| GGUF | Q3_K_L | 4.5 | |

| GGUF | IQ4_XS | 4.7 | |

| GGUF | Q4_K_S | 4.9 | fast, recommended |

| GGUF | Q4_K_M | 5.1 | fast, recommended |

| GGUF | Q5_K_S | 5.8 | |

| GGUF | Q5_K_M | 6.0 | |

| GGUF | Q6_K | 6.8 | very good quality |

| GGUF | Q8_0 | 8.8 | fast, best quality |

| GGUF | f16 | 16.5 | 16 bpw, overkill |

Here is a handy graph by ikawrakow comparing some lower-quality quant

types (lower is better):

!image.png

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.

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