ProgressGym-HistLlama3-70B-C013-instruct-v0.1-GGUF – AI Model by mradermacher | AlphaNeural AI

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mradermacher/
ProgressGym-HistLlama3-70B-C013-instruct-v0.1-GGUF

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PKU-Alignment/ProgressGym-HistLlama3-70B-C013-instruct-v0.1

PKU-Alignment/ProgressGym-HistText

PKU-Alignment/ProgressGym-TimelessQA

endpoints_compatible

value alignment

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About

static quants of https://huggingface.co/PKU-Alignment/ProgressGym-HistLlama3-70B-C013-instruct-v0.1

weighted/imatrix quants are available at https://huggingface.co/mradermacher/ProgressGym-HistLlama3-70B-C013-instruct-v0.1-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	26.5
GGUF	IQ3_XS	29.4
GGUF	IQ3_S	31.0	beats Q3_K*
GGUF	Q3_K_S	31.0
GGUF	IQ3_M	32.0
GGUF	Q3_K_M	34.4	lower quality
GGUF	Q3_K_L	37.2
GGUF	IQ4_XS	38.4
GGUF	Q4_K_S	40.4	fast, recommended
GGUF	Q4_K_M	42.6	fast, recommended
GGUF	Q5_K_S	48.8
GGUF	Q5_K_M	50.0
PART 1 PART 2	Q6_K	58.0	very good quality
PART 1 PART 2	Q8_0	75.1	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.