mradermacher/ALIA-40b-instruct-2601-GGUF

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base_model: BSC-LT/ALIA-40b-instruct-2601 datasets:

• CohereLabs/ayadataset
• projecte-aina/CoQCat
• databricks/databricks-dolly-15k
• projecte-aina/dolly3kca
• projecte-aina/MentorES
• projecte-aina/MentorCA
• HuggingFaceH4/norobots
• projecte-aina/RAGMultilingual
• Unbabel/TowerBlocks-v0.2
• OpenAssistant/oasst2
• open-r1/OpenR1-Math-220k
• HuggingFaceFW/fineweb-edu
• allenai/WildChat-1M

language:

• ca
• en
• es
• eu
• gl

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

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About

static quants of https://huggingface.co/BSC-LT/ALIA-40b-instruct-2601

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

weighted/imatrix quants are available at https://huggingface.co/mradermacher/ALIA-40b-instruct-2601-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	15.8
GGUF	Q3K_S	18.3
GGUF	Q3K_M	20.1	lower quality
GGUF	Q3K_L	21.7
GGUF	IQ4_XS	22.4
GGUF	Q4K_S	23.5	fast, recommended
GGUF	Q4K_M	24.7	fast, recommended
GGUF	Q5K_S	28.2
GGUF	Q5K_M	28.9
GGUF	Q6_K	33.3	very good quality
GGUF	Q8_0	43.1	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.