@wassemgtk
— Thank you for your brilliant work on Graph-Enhanced Singular Adaptive Learning (GESAL) and for releasing the code and white paper on GitHub. Your framework’s innovative approach—leveraging Singular Value Factorization (SVF), graph memory, and reinforcement learning (RL) for real-time LLM adaptation—is truly impressive, efficient, and fast. I’ve adapted GESAL for synthetic engine/turbine data for wear prediction and predictive maintenance, as referenced in @oieieio1’s X update, and it’s shown promising results, though I’ve noticed prompt design has a massive impact on outcomes.
My Implementation
I’m using GESAL with meta-llama/Llama-3.2-1B on an Ubuntu 24.04 system with:
- Hardware: 4060Ti GPU (16GB VRAM), Xeon processors (36 cores), 256GB RAM
- Software: InfluxDB 3 Core (version 0.1.0 alpha) for storing and querying ~20 engine parameters, including:
flight_hoursexhaust_tempvibrationrpmthrustpressurewear- (and others like
fuel_flow, oil_temp, etc.)
My latest analysis of 1,326 synthetic engines shows:
- Nodes: 2 unique nodes
- Accuracy: 22.48% on maintenance predictions (keywords: “replace,” “maintenance,” “check”)
- Earlier Results: 42.0% accuracy and 189 nodes with fewer engines (still experimenting with prompts).
For example, a sample input is:
Engine 1—flight_hours=5000h, exhaust_temp=750°C, vibration=0.5g, rpm=12000, thrust=75kN, pressure=1.5bar, wear=3.2. Predict maintenance.
We’re actively experimenting with prompts—e.g., “Predict maintenance” vs. “Identify maintenance needs for wear and predict ‘replace’, ‘maintenance’, or ‘check’”—and finding that slight changes dramatically affect GESAL’s responses, likely due to temperature=0.7 and top_k=50 in generation.
Goals and Challenges
While GESAL’s scalability is excellent, I’m targeting:
- Accuracy: 70-80%
- Nodes: 10-15
The drop from 42.0% to 22.48% accuracy may stem from prompt variations or scaling effects, possibly amplified by current hyperparameters.
Seeking Feedback
I’d appreciate your insights on:
- Optimizing prompts for consistency and accuracy
- Tuning hyperparameters (e.g.,
temperature, top_k, distance_threshold)
- Scaling GESAL for large industrial datasets (e.g., thousands of engines)
- Benchmarking GESAL for mechanical systems, as
@hassenhamdi
mentioned
The attached visualization (Wear vs Exhaust Temperature) shows current performance—I’m eager to collaborate further to refine prompts and boost accuracy!
Thanks again for this groundbreaking tool—I’m excited to see how GESAL can evolve!
