mradermacher/atom-80b-GGUF

---

base_model: vanta-research/atom-80b language:

• en

library_name: transformers license: apache-2.0 mradermacher: readme_rev: 1 quantized_by: mradermacher tags:

• qwen3
• qwen3-next
• qwen
• vanta-research
• cognitive-configuration
• text-generation
• instruction-following
• cognitive-ai
• large-language-model
• helpful-ai
• aligned-ai
• philosophical
• emotional-intelligence
• atom
• collaborative-ai
• collaboration
• conversational
• conversational-ai
• alignment-ai
• chat
• reasoning
• friendly
• ai-research
• ai-alignment-research
• ai-alignment
• ai-behavior-research
• ai-collaboration-research

---

About

static quants of https://huggingface.co/vanta-research/atom-80b

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/atom-80b-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	29.2
GGUF	Q3K_S	34.6
GGUF	Q3K_M	38.3	lower quality
GGUF	Q3K_L	41.3
GGUF	IQ4_XS	43.2
GGUF	Q4K_S	45.6	fast, recommended
GGUF	Q4K_M	48.5	fast, recommended
GGUF	Q5K_S	55.1
GGUF	Q5K_M	56.8
GGUF	Q6_K	65.6	very good quality
GGUF	Q8_0	84.9	fast, best quality

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.