CareerLens
2024–2025Career counselling meetings, turned into a summary and a to-do list
A bot joins the counselling call and what comes back is a speaker-labelled transcript. The model turns that into a fixed JSON payload (summary, action items, insights, who spoke) in whichever of English, Hindi or Marathi the session ran in. The model is the project’s own: three open-weight families were benchmarked on a hand-built dataset of counselling transcripts, and the best of them, Llama 3.1 8B, was fine-tuned with Unsloth and LoRA until it beat the others on quality and inference time at once.
- MeetingA bot joins the call as a participant
- TranscriptSpeaker-labelled turns, with durations
- Fine-tuned LLMLlama 3.1 8B, 4-bit, LoRA adapter
- JSON payloadSummary, action items, insights, speakers
- DashboardReview, talk-time split, ask follow-ups
- Python
- Unsloth + PEFT
- Llama 3.1 8B
- Flask
- FastAPI
- React
- DynamoDB