Run LLM AI Model in Local machine with Zero effort (No Internet needed)⚡️
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- Опубликовано: 11 июн 2024
- In this video, we will discuss running Large Language Model in our Local machine with ZERO Effort with no internet needed once model is fully downloaded.
Use LLMs like
→ LLAMA
→ LLAMA 2
→ MISTRAL 2
→ GPT 3.5
→ GPT 4 and more.
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Can we consider the indexing process as equivalent to training?
That’s correct
Not exactly. The indexing process just takes all your PDF text and turns it into vectors (think it as a numerical representation of the PDF content in a vector space) using an embedding model, in this case, it's the SBert model. When we ask a question, our query is also turned into vectors. Now that our query and the PDF content are in vector form, it's easier for the embedding model to perform a similarity search to find the top relevant results from the PDF.
These similar results, along with our query (both in the text format), get sent to the language model like Llama, so we get a spot-on answer without any unnecessary or irrelevant stuff. As you can see, we’re not retraining our base model here, we’re simply using it to extract accurate answers from the top results. This process is called "Retrieval-augmented generation (RAG)". You can check it out more online if you’re curious.
@@kishanlal676 Cool makes sense, in the current context, this indexing is nothing but pacing up the retrieval process just like Windows search indexing or even mongo db
@@kafkaesqued Exactly, we cannot send/upload a whole PDF to LLM. It may take some time or it could error out due to token limitations. So we are using vector search to get some portion of the document where the answer may reside and provide that portion of PDF to LLM to get the exact answer for our question.
Can we use this as apis to call inside code?