Transformers
GGUF
text-generation-inference
unsloth
llama
trl
sft
Inference Endpoints
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metadata
base_model: mpasila/Viking-SlimSonnet-v0.2-7B
datasets:
  - Gryphe/Sonnet3.5-SlimOrcaDedupCleaned
  - mpasila/Sonnet3.5-SlimOrcaDedupCleaned-4k-context
language:
  - en
  - fi
  - sv
  - 'no'
  - da
  - is
  - nn
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - trl
  - sft

About

static quants of https://huggingface.co/mpasila/Viking-SlimSonnet-v0.2-7B

weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.

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 3.1
GGUF IQ3_XS 3.4
GGUF IQ3_S 3.6 beats Q3_K*
GGUF Q3_K_S 3.6
GGUF IQ3_M 3.7
GGUF Q3_K_M 3.9 lower quality
GGUF Q3_K_L 4.2
GGUF IQ4_XS 4.3
GGUF Q4_K_S 4.5 fast, recommended
GGUF Q4_K_M 4.7 fast, recommended
GGUF Q5_K_S 5.4
GGUF Q5_K_M 5.5
GGUF Q6_K 6.3 very good quality
GGUF Q8_0 8.1 fast, best quality
GGUF f16 15.2 16 bpw, overkill

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.