ThermalGuardV-v1_1 is a Vision-Language Model (VLM) based on Qwen2.5-VL-7B-Instruct, fine-tuned with LoRA (Low-Rank Adaptation) and fully merged for direct deployment. It excels in materials science domains, particularly:
-
Thermal Barrier Coatings (TBCs)
-
High-Entropy Alloys (HEAs)
-
High-Temperature Oxidation
This model has been enhanced with technical knowledge about advanced materials for high-temperature applications, including composition design, microstructure characterization, performance evaluation, and failure mechanisms.
-
Thermal barrier coatings (YSZ, gadolinium zirconate, etc.)
-
High-entropy alloy systems (CoCrFeMnNi, refractory HEAs, etc.)
-
High-temperature oxidation mechanisms
-
Base Model: Qwen2.5-VL-7B-Instruct
-
Fine-tuning Method: LoRA (Low-Rank Adaptation), later fully merged
-
Learning Rate: 0.0001
-
Batch Size: 4 (effective size 8 with gradient accumulation)
-
Epochs: 3
-
Optimizer: AdamW (β₁=0.9, β₂=0.999, ε=1e-08)
-
Scheduler: Cosine learning rate schedule
-
Mixed Precision: Native AMP
The
dataset was used to evaluate several recent models with the following qualitative observations(batch_size=8):
This model (ThermalGuardV) is a research-oriented AI tool independently developed for materials science applications. The model's outputs should be considered as informational suggestions rather than professional advice, and users are advised to verify critical materials science information through authoritative sources.
