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@@ -45,17 +45,15 @@ to see all files<br>
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  <b> the setup for local documents described below is allmost the same, GPT4All has only one model (nomic), and koboldcpp and JAN(Menlo) is not build in right now but in development</b><br>
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  (sometimes the results are more truthful if the “chat with document only” option is used)<br>
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- BTW the embedder-model is only a part of a good RAG (Retrieval-Augmented Generation)<br>
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  <b>&#x21e8;</b> give me a ❤️, if you like ;)<br>
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  <br>
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  <b>My short impression:</b>
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  <ul style="line-height: 1.05;">
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- <li>nomic-embed-text (up to 2048t context length)</li>
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- <li>mxbai-embed-large</li>
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  <li>mug-b-1.6</li>
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  <li>snowflake-arctic-embed-l-v2.0 (up to 8192t context length)</li>
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- <li>ger-RAG-bge-M3-merg-snowf-artic-hessian-AI (german, up to 8192t context length)</li>
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- <li>german-roberta</li>
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  <li>bge-m3 (up to 8192t context length)</li>
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  </ul>
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  Working well, all other its up to you! Some models are very similar! (jina and qwen based you can add manual to LM-Studio, set model "gear wheel" below "overide domain type")<br>
@@ -64,6 +62,10 @@ Further tests have shown that the following models are suitable for complex task
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  <ul style="line-height: 1.05;">
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  <li>GTE large</li>
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  <li>cross-en-de-es-roberta</li>
 
 
 
 
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  </ul>
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  There are two embedder to find toxic content (toxic-prompt-roberta and minilmv2-toxic-jigsaw), dont know how good it works, and from ibm it give a whole LLM model (granite-guardian).
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  <br>
 
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  <b> the setup for local documents described below is allmost the same, GPT4All has only one model (nomic), and koboldcpp and JAN(Menlo) is not build in right now but in development</b><br>
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  (sometimes the results are more truthful if the “chat with document only” option is used)<br>
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+ BTW the embedder-model is only a part of a good RAG (Retrieval-Augmented Generation), 512t are ~2000 characters most cases enough.<br>
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  <b>&#x21e8;</b> give me a ❤️, if you like ;)<br>
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  <br>
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  <b>My short impression:</b>
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  <ul style="line-height: 1.05;">
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+ <li>nomic-embed-text-v2-moe (up to 512t context length)</li>
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+ <li>mxbai-embed-large (small and fast model)</li>
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  <li>mug-b-1.6</li>
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  <li>snowflake-arctic-embed-l-v2.0 (up to 8192t context length)</li>
 
 
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  <li>bge-m3 (up to 8192t context length)</li>
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  </ul>
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  Working well, all other its up to you! Some models are very similar! (jina and qwen based you can add manual to LM-Studio, set model "gear wheel" below "overide domain type")<br>
 
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  <ul style="line-height: 1.05;">
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  <li>GTE large</li>
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  <li>cross-en-de-es-roberta</li>
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+ <li>ger-RAG-bge-M3-merg-snowf-artic-hessian-AI (very good for german, up to 8192t context length)</li>
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+ <li>German-RAG-BGE-M3-TRIPLES-HESSIAN-AI (very good for german, up to 8192t context length)</li>
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+ <li>bge-m3 (good for german, up to 8192t context length)</li>
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+ <li>jina-embeddings-v3 (good for german, up to 8192t context length)</li>
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  </ul>
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  There are two embedder to find toxic content (toxic-prompt-roberta and minilmv2-toxic-jigsaw), dont know how good it works, and from ibm it give a whole LLM model (granite-guardian).
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  <br>