We've started adding loaders to help load data in a format such that you can finetune (usually for a custom tone) So far we've got Facebook Messenger, Slack, Telegram, WhatsApp, Twitter (via @apify
), Discord What else should we add?
@hwchase17
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New Data Loaders for Fine-tuning AI Models Across Platforms
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LangChain Twitter Fine-tuning Repository Released
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In the github repo! https://
github.com/langchain-ai/t
witter-finetune
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Training AI models effectively with minimal labeled data
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was <100 examples, and i actually didnt even have a topic for each tweet…. it seems to work fine without topics (which I was a bit surprised by), if it hadnt i probably would have generated them with AI
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RAG Webinar Features Real-World Application Builder Insights
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Part of what made our webinar on RAG so great in my mind was that we had an application builder (Pedro from Tavrn) on to talk about what he ACTUALLY does in practice If you are an app builder and want to join a future webinar – reach out! Webinar:
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Apify enables Twitter data integration with LangChain for AI
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@apify can do this pretty well! Example here: https://
python.langchain.com/docs/integrati
ons/chat_loaders/twitter
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Streamlit Twitter Clone App with LangChain Fine-tuning Exploration
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Streamlit app so you can play around with yourself here: https://
elon-twitter-clone.streamlit.app Code: https://
github.com/langchain-ai/t
witter-finetune
… Going to be exploring this in a webinar next week with @GregKamradt
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Fine-tuning Models for Custom Tone: Elon Musk Example
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One of my favorite use cases for fine-tuning is getting a model to speak in a custom tone This is kinda hard to achieve with just prompting/few-shot Here's an example of fine-tuning a model to tweet like Elon Musk (compare it to what you get by just prompting)
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Redis Filter Expression Language and Metadata Indexing Improvements
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There's some REALLY cool improvements here My two favorites: Automatic Metadata Indexing and support for Redis Filter Expression Language
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Retrieval Systems Fetch Parent Documents for Enhanced Context
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Usually retrieval system then fetch each chunk individually. The idea behind this is you fetch the parent document they come from (for more context)
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LangChain releases chat loaders for Llama fine-tuning event
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If you are in SF tmrw… there is a great event happening tmrw around finetuning Llama In anticipation of that we released some new loaders to help load chat data into an easy-to-use format: https://
blog.langchain.dev/chat-loaders-f
inetune-a-chatmodel-in-your-voice/
… Join the event here: