We tested and evaluated different models based on their tokenization, latency, and accuracy. Gemma-7b is the most consistent model in terms of text extraction, and it was trained on resume datasets, which helps reduce the hallucinations.

• Johnny

⭐ Review of TalentLensAI ⭐

Architecture:
TalentLensAI uses OpenAI’s GPT-4o for extracting detailed information from resumes. It also uses Hugging Face models like facebook/bart-large-cnn for summarizing resumes and google/gemma-7b for scoring candidates. This smart mix ensures accurate and reliable results even if some models slow down.

Features:
The app is built with Streamlit, offering a simple and clear interface. It stores data securely using Supabase and allows users to edit parsed resume details before formatting. The resume templates create neat, professional documents in .docx format, ready to use. It also shows helpful info like how many skills were found and a quality score for each resume.

Usability:
Using TalentLensAI is straightforward: upload your resume, review and edit the parsed info if needed, then download a formatted resume easily. The tool saves a lot of manual work and is friendly even for users without technical skills.

Final Impression:
I’ve tried TalentLensAI and am impressed by how well it understands and formats resumes. The mix of advanced AI models with practical design makes it a true game-changer for recruiters and job seekers alike. Great job to the team for building such an innovative and useful app!

I have tried TalentLensAI and its impressive how it evaluates and formats candidates resumes. With the use of AI models, this makes Candidate Engagement by personalizing prompts and the model makes the interview feel less transactional and more conversational, encouraging richer responses. Recruiters can screen candidates more effectively by letting the LLM handle repetitive, initial qualification checks before human intervention. Very promising solution.