---
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

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