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README.md
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- Granite
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- BGE
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- Jina
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- Snowflake
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- Qwen
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- text-embeddings-inference
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# <b>All models tested with ALLM(AnythingLLM) with LM-Studio as server, all models should be work with ollama</b>
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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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Incidentally, the Embedder model is only one part of a good RAG (Retrieval-Augmented Generation), but it should be tailored to your language and, if you want it to be completely accurate, also to the application, e.g. programming or medicine. <br>
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- Granite
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- BGE
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- Jina
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- gemma
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- Snowflake
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- Qwen
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- text-embeddings-inference
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# <b>All models tested with ALLM(AnythingLLM) with LM-Studio as server, all models should be work with ollama</b>
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| 46 |
<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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I would always use f32 or f16 bit quality!<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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Incidentally, the Embedder model is only one part of a good RAG (Retrieval-Augmented Generation), but it should be tailored to your language and, if you want it to be completely accurate, also to the application, e.g. programming or medicine. <br>
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