mradermacher/Meta-Llama-3.1-8B-Instruct_CODE_Python_English_Asistant-16bit-v2-GGUF

About

static quants of https://huggingface.co/Agnuxo/Meta-Llama-3.1-8B-Instruct_CODE_Python_English_Asistant-16bit-v2

weighted/imatrix quants are available at https://huggingface.co/mradermacher/Meta-Llama-3.1-8B-Instruct_CODE_Python_English_Asistant-16bit-v2-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	3.3
GGUF	IQ3_XS	3.6
GGUF	Q3_K_S	3.8
GGUF	IQ3_S	3.8	beats Q3_K*
GGUF	IQ3_M	3.9
GGUF	Q3_K_M	4.1	lower quality
GGUF	Q3_K_L	4.4
GGUF	IQ4_XS	4.6
GGUF	Q4_K_S	4.8	fast, recommended
GGUF	Q4_K_M	5.0	fast, recommended
GGUF	Q5_K_S	5.7
GGUF	Q5_K_M	5.8
GGUF	Q6_K	6.7	very good quality
GGUF	Q8_0	8.6	fast, best quality
GGUF	f16	16.2	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.