mradermacher's picture
auto-patch README.md
69b4ee2 verified
metadata
base_model:
  - cognitivecomputations/dolphin-2.2-70b
  - WizardLM/WizardMath-70B-V1.0
  - migtissera/SynthIA-70B-v1.2b
  - epfl-llm/meditron-70b
language:
  - en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
  - mergekit
  - merge

About

weighted/imatrix quants of https://huggingface.co/abacusai/TheProfessor-155b

static quants are available at https://huggingface.co/mradermacher/TheProfessor-155b-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 i1-IQ1_S 32.8 for the desperate
GGUF i1-IQ2_XXS 41.3
GGUF i1-IQ2_XS 45.9
PART 1 PART 2 i1-IQ2_M 52.3
PART 1 PART 2 i1-Q2_K 57.1 IQ3_XXS probably better
PART 1 PART 2 i1-IQ3_XXS 60.6 fast, lower quality
PART 1 PART 2 i1-Q3_K_XS 63.2
PART 1 PART 2 i1-IQ3_XS 63.3
PART 1 PART 2 i1-Q3_K_S 66.9 IQ3_XS probably better
PART 1 PART 2 i1-Q3_K_M 74.7 IQ3_S probably better
PART 1 PART 2 i1-Q3_K_L 81.3 IQ3_M probably better
PART 1 PART 2 i1-Q4_K_S 88.1 optimal size/speed/quality
PART 1 PART 2 i1-Q4_K_M 93.1 fast, medium quality
PART 1 PART 2 PART 3 i1-Q5_K_S 106.7
PART 1 PART 2 PART 3 i1-Q5_K_M 109.6
PART 1 PART 2 PART 3 i1-Q6_K 127.2 practically like static Q6_K

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

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.