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--- |
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library_name: sentence-transformers |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- embedder |
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- embedding |
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- moedels |
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- GGUF |
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- text-embeddings-inference |
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misc: |
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- text-embeddings-inference |
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language: |
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- en |
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- de |
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--- |
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# All models tested with ALLM(AnythingLLM) with LM as server |
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they work more or less |
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my short impression: |
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- nomic-embed-text |
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- mxbai-embed-large |
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- mug-b-1.6 |
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working well, all other its up to you! |
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short hints for using: |
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set your (Max Tokens)context-lenght 16000t main-model, set your embedder-model (Max Embedding Chunk Length) 1024t,set (Max Context Snippets) 14 |
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-> ok what that mean! |
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you can receive 14-snippets a 1024t (14336t) from your document ~10000words and 1600t left for the answer ~1000words |
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you can play and set for your needs, eg 8-snippets a 2048t, or 28-snippets a 512t ... |
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16000t ~1GB VRAM usage |
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... |
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... |
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... |
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(ALL Licenses and terms of use go to original author) |
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