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---
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)
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