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Please look at sketch and langchain pandas/SQL plugins. I have seen excellent results with both of these approaches. Both of these approaches will require you to send metadata to openAI.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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llama-peft-tuner
Tune LLaMa-7B on Alpaca Dataset using PEFT / LORA Based on @zphang's https://github.com/zphang/minimal-llama scripts.
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So as far as set up goes, you just need to: “”” Git clone https://github.com/lxe/simple-llama-finetuner Cd simple-llama-finetuner Pip install -r requirements.txt Python app.py ## if you’re on a remote machine (Paperspace is my go to) then you may need to edit the last line of this script to set ‘share=True’ in the launch args “””
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sidekick
Discontinued Universal APIs for unstructured data. Sync documents from SaaS tools to a SQL or vector database, where they can be easily queried by AI applications [Moved to: https://github.com/psychic-api/psychic] (by ai-sidekick)
A project I’m working on helps with ETL for retrieval augmented generation: https://github.com/ai-sidekick/sidekick
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azure-search-openai-demo
A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
What some people have done is to use Azure Cognitive Search as a pre-cursor to the LLM.
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It's really not helpful to make strong assertions like this without referring to specific, verifiable sources. Fine-tuning very typically is done in a way where certain layers/parameters of the model are frozen. This is done to avoid the sort of loss we are discussing. The LoRA paper itself states that LoRA "freezes the pre-trained model weights".
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xTuring
Build, customize and control you own LLMs. From data pre-processing to fine-tuning, xTuring provides an easy way to personalize open-source LLMs. Join our discord community: https://discord.gg/TgHXuSJEk6
I'm currently working on an open-source project for building and controlling LLMs: https://github.com/stochasticai/xturing