metadata

library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - embedder
  - embedding
  - moedels
  - GGUF
  - text-embeddings-inference
misc:
  - text-embeddings-inference
language:
  - en
  - de

All models tested with ALLM(AnythingLLM) with LM as server

they work more or less

my short impression:

nomic-embed-text
mxbai-embed-large
mug-b-1.6

working well, all other its up to you!

short hints for using: set your (Max Tokens)context-lenght 16000t main-model, set your embedder-model (Max Embedding Chunk Length) 1024t,set (Max Context Snippets) 14

-> ok what that mean!

you can receive 14-snippets a 1024t (14336t) from your document ~10000words and 1600t left for the answer ~1000words

you can play and set for your needs, eg 8-snippets a 2048t, or 28-snippets a 512t ...

16000t ~1GB VRAM usage

...

(ALL Licenses and terms of use go to original author)