I tested it with problems from my university exams (curve length, extrema, gradient fields, 2D quantum box). Calculus worked surprisingly well.
I might also test it on matrix-related problems in the future.
Running fully local on AMD + ROCm.
Model quirks
- Curve length: Interprets inputs context-sensitively, e.g., e-t is correctly read as e^(-t).
- Gradient fields: Often overshoots and automatically computes the antiderivative; needed a stop condition.
- Step-by-step: Solves problems in a very textbook-like manner; some steps could be skipped when doing calculations by hand.
Video workflow:
- Recorded with OBS
- Edited in Kdenlive
- Transcoded with VAAPI (H.264)
No cloud, real hardware.
Everything runs on Linux + Text Generation Web UI (FOSS), so anyone can set this up.
No GPU? No problem, you can also run it using PyTorch’s CPU backend, just much slower.
Background music: Evanescence - Haunted (https://www.youtube.com/watch?v=tjDlL87sHMw)
#LocalAI #LLM #Qwen #MathAI #FOSS #GenerativeAI #Linux #ROCm #math

AI底層數據建模








