I’ve reviewed 2000+ resumes for machine learning roles in the last 5 years. Here are 7 tips to make your resume stand out:
@saboo_shubham_
-
Chat with PDF LLM Applications Repository Guide
By
–
Please check the instructions here: https://
github.com/Shubhamsaboo/a
wesome-llm-apps/tree/main/chat_with_pdf
… -
Hardware Configuration Impact on AI Code Performance
By
–
Totally dependent on the config of your local machine like RAM, GPU, etc. It has nothing to do with the code.
-
LLM Tips and Tutorials for AI Enthusiasts
By
–
If you find this useful, RT to share it with your friends. Don't forget to follow me @Saboo_Shubham_ for more such LLMs tips and tutorials.
-
Awesome LLM Apps with RAG GitHub Repository
By
–
Find all the awesome LLM Apps demo with RAG in the following Github Repo. P.S: Don't forget to star the repo to show your support
-
Streamlit PDF Chat Application Demo with Llama3
By
–
Working Application demo using Streamlit
— Shubham Saboo (@Saboo_Shubham_) 23 mai 2024
Paste the above code in vscode or pycharm and run the following command: 'streamlit run chat_pdf_llama3.py' pic.twitter.com/cnyYCaFIfSWorking Application demo using Streamlit Paste the above code in vscode or pycharm and run the following command: 'streamlit run chat_pdf_llama3.py'
-
Full RAG Application Code to Chat with PDF using Llama-3
By
–
Full RAG Application Code to Chat with PDF using Llama-3
-
Build PDF Q&A App Using Embedchain and Streamlit
By
–
6. Ask question about the PDF and display the answer • Create a text input for the user to enter their question using 'st.text_input()'
• If a question is asked, get the answer from the Embedchain app and display it using 'st.write()' -
Upload PDF to Knowledge Base Using Streamlit File Uploader
By
–
5. Upload a PDF file from UI and add it to the knowledge base • Use 'st.file_uploader()' to create a file uploader for PDF files.
• If a PDF file is uploaded, create a temporary file and write the contents of the uploaded file to it. -
Initializing Embedchain App with Vector Database Setup
By
–
4. Initialize the Embedchain App • Create a temporary directory for the vector database using 'tempfile.mkdtemp()'
• Create an instance of embedchain bot.