#Best Model For LocalDocs?

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broken turret
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Which language model is best trained on PDFs and can effectively handle tasks such as searching, indexing, and question-answering based on PDF content?

upbeat laurelBOT
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Hi @broken turret. I'm kapa.ai, your friendly support bot. I will try to answer your question by searching through the knowledge base. :wave:

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Based on the provided knowledge sources, there isn't specific information about which language model is best trained on PDFs or most effective for tasks like searching, indexing, and question-answering based on PDF content within the context of GPT4All's LocalDocs feature. However, I can provide some relevant information about LocalDocs and model recommendations:

  1. LocalDocs Support: GPT4All's LocalDocs feature supports PDF files (.pdf) along with plain text files (.txt, .md, and .rst) GPT4All Documentation.

  2. Recommended Model: While not specifically for PDF handling, the documentation suggests that Llama 3 Instruct performs best for chatting with files in LocalDocs GPT4All Documentation.

  3. Embedding Model: LocalDocs uses Nomic AI's free and fast on-device embedding models to index your folder into text snippets with embedding vectors GPT4All Documentation.

It's important to note that the effectiveness of the model for PDF-related tasks may depend on various factors, including the specific content of the PDFs and the nature of the questions being asked. The LocalDocs feature is designed to work with various file types, including PDFs, but the knowledge sources don't provide a definitive answer about which model is best specifically for PDF handling.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@broken turret Kapa.ai is still learning and improving, please let me know how I did by reacting below.

broken turret
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i thought mistral would be or snoozy

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@upbeat laurel i thought mistral would be

upbeat laurelBOT
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Let me check the knowledge base for you again. One moment! :books:

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Based on the knowledge sources provided, there isn't specific information stating that Mistral would be the best model for LocalDocs or PDF handling. However, I can provide some relevant information about model recommendations for GPT4All and LocalDocs:

  1. The documentation specifically mentions that "Llama 3 Instruct performs best" for chatting with files in LocalDocs GPT4All Documentation.

  2. While Mistral-based models are supported by GPT4All, there's no explicit statement in the provided sources indicating that Mistral is superior for LocalDocs or PDF processing.

  3. One Mistral-based model mentioned in the sources is "Nous Hermes 2 Mistral DPO," which is listed as an example model GPT4All Documentation. However, its performance specifically for LocalDocs or PDF handling is not discussed.

It's important to note that the effectiveness of a model can vary depending on the specific task and content. If you have personal experience or information suggesting that Mistral performs well for your specific use case with LocalDocs, it might be worth trying. However, based solely on the provided knowledge sources, there's no definitive statement supporting Mistral as the best choice for LocalDocs or PDF processing.

Note: you can ask me a follow up question by @ mentioning me again :speech_balloon:

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@broken turret Kapa.ai is still learning and improving, please let me know how I did by reacting below.

lusty blaze
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I think, since Gpt4All creates their own embeddings and everything, that the primary differentiator to handle more things should be context size (apart from model capabilities in general). The model itself isn't actually doing anything with the local docs, Gpt4all is passing that into the conversation context after performing a similarity search on the question. So the way it generates the doc embeddings doesn't matter as much as how you phrase the question.

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For RAG in general, it just does that initial search on the documents to grab 'most likely relevant' extra context before passing additional data to the model when you ask a question.

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So models that handle large contexts well will generally be better, there's a benchmark for that, let me find it...

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Multistep Soft Reasoning (MuSR):
Scope: Reasoning and understanding on/of long texts
Language understanding
Reasoning capabilities
Long context reasoning

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So in general, models that score higher there would be better for it (if all other things were equal)

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I would generally recommend a high IFEval score too, though, since that relates to how well you can instruct the model to do specific things.

broken turret
# lusty blaze I think, since Gpt4All creates their own embeddings and everything, that the pri...

Hey Sea im a new programmer i use claude and cursor and windsurf ect ect. i just want this app for quick chats over pdf's idk much about hugging face but qwen is up there. thanks man for all the help. i dont plan on using this for coding but for things like downloading a bunch of wiki's and guides and asking questions from there. should i just stick to qwen? i really dont know what all the fellas are using this app for.

lusty blaze
# broken turret Hey Sea im a new programmer i use claude and cursor and windsurf ect ect. i just...

Finding the best model that you can run locally depends on a lot of factors. If you have a GPU with 8GB of VRAM, for general purpose then models like Virtuoso Small V2, Qwen 14B, Microsoft Phi 4 Reasoning, or Lamarck 14B are all worth checking out. I don't think Falcon 3 10B runs on GPT4All, but it's also worth trying on something like LM Studio. They're all capable of having conversations with RAG on PDFs - that works by running a search locally for relevant content snippets in the documentation, then passing those snippets into the LLM context when you ask a question along with the question. So it won't work well if the documentation is too large. I generally add just a few domain-specific documents that I know are relatively recent/accurate when chatting about things using that method, rather than index every document in one big collection. You'd need a model that supports a very large context if you have a lot of documents, then modify the local docs settings for snippet size and snippets # per prompt accordingly.

For coding, Qwen 2.5 Coder 14B or DeepSeek Coder V2 Lite Instruct remain some of the best options for coding that can run in 8GB of VRAM. To run Qwen 2.5 Coder 32B, which regularly scores near the top for coding (of open models you can run locally at least), you'd need 24 GB of VRAM for Q5.. which means only people with top of the line GPUs can run it locally. Claude/Cursor/Windsurf will all generally do better than Qwen 2.5 Coder for coding, as Qwen is a smaller model (but very capable for its size).