Upload Documents and Change Models in Local GPT 🚀 | LocalGPT Configuration Guide @LocalGPT
"Uploading Documents and Modifying Models in Local GPT 🚀 | LocalGPT Configuration Tutorial" | simplify ai | trending | viral | #privategpt #deep #ai #machinelearning #techtutorial
Important links :
🔗 Mini-conda Installation : • Download Miniconda on Windows: Error-...
🔗Local GPT Installation (Part-1) : • Zero ERROR Installation Guide to Loca...
🔗 Run Local GPT : • Run Local GPT on Windows Without ❌ Er...
🔗 Website Link : https://simplifyai.in
🔗 Local GPT Playlist : • RAG | LocalGPT
Commands :
-- Python Ingest Command:
python ingest.py
-- NLP Library Command:
import nltk
nltk.download('punkt')
-- Python Run Local GPT Command:
python run_LocalGPT.py
-- Commands to Install LlamaCPP:
set CMAKE_ARGS=-DLLAMA_CUBLAS=on FORCE_CMAKE=1
pip install llama-cpp-python==0.1.83 --no-cache-dir
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Description :
In this video, we dive into running Local GPT on your system, addressing common errors faced during the process. Before we start, I'll recap what we covered in our previous videos, including the error-free installation of Miniconda and Local GPT. All the essential links and commands will be provided in the description for your convenience.
Navigate to our "pro" folder where the Local GPT folder is located. Open your terminal and ensure you've watched our Part 1 video on Local GPT installation, as certain packages are required.
Inside the Local GPT folder using the CD command, our Conda environment is activated, making us ready to run Local GPT on our system.
To embed your system's text into a vector database, we use the "python ingest.py" command. This process utilizes the Orca paper PDF to import data, cutting or splitting the document into smaller chunks for efficient analysis.
Encountering a "list index out of range" error during this process? It may be due to missing Natural Language Processing (NLP) libraries like NLTK. Resolve this by installing NLP libraries in the "ingest.py" file.
Now, head to the last command. Run Local GPT using "python run/LocalGPT.py," and you'll see the model start installing. Note that our LLM models are quantized (GGUF and GGML formats) and use LlamaCPP to run. Installation may throw an error if LlamaCPP is missing.
To install LlamaCPP, use two commands: "set Cmake/ARGS equals to -D llama" and "pip install LlamaCPP." Followed by running the Local GPT command again, overcoming the LlamaCPP error successfully.
The Llama architecture, with 7 billion parameters and a size of 6.74 billion, runs on CPU (Blas equals zero). In the prompt area, input queries like "What is the Orca model in the paper?" and observe the model's CPU usage for the answer.
This is the second part; in the third, we'll explore configuring Local GPT, changing models, and managing documents. Stay tuned for an in-depth tutorial!
Hashtags :
#LocalGPT
#PythonProgramming
#AIInstallation
#NLPProcessing
#ErrorResolution
#CondaEnvironment
#LlamaCPP
#DocumentAnalysis
#CPUOptimization
#YouTubeTutorial
Tags -
chatgpt, ai, artificial intelligence, privategpt, private gpt, chat with files, open-source gpt, open source llm, gpt4, gpt3.5, chat gpt, open ai, gpt4all, gpt 4 all, tutorial, llm tutorial
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