Atomight-V2.1-0.5B-Inference-GGUF/README.md

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

base_model	datasets	language	library_name	license	mradermacher	quantized_by	tags
NovatasticRoScript/Atomight-V2.1-0.5B-Inference	openai/gsm8k openai/openai_humaneval cais/mmlu allenai/ai2_arc bespokelabs/Bespoke-Stratos-17k	en	transformers	mit	readme_rev 1	mradermacher	text-generation causal-lm grpo reasoning reinforcement-learning mini-llm text-generation-inference lm-evaluation-harness

About

static quants of https://huggingface.co/NovatasticRoScript/Atomight-V2.1-0.5B-Inference

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Atomight-V2.1-0.5B-Inference-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	Q3_K_S	0.4
GGUF	Q2_K	0.4
GGUF	IQ4_XS	0.5
GGUF	Q3_K_M	0.5	lower quality
GGUF	Q3_K_L	0.5
GGUF	Q4_K_S	0.5	fast, recommended
GGUF	Q4_K_M	0.5	fast, recommended
GGUF	Q5_K_S	0.5
GGUF	Q5_K_M	0.5
GGUF	Q6_K	0.6	very good quality
GGUF	Q8_0	0.6	fast, best quality
GGUF	f16	1.1	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.