codewithdark / EVLMs-v2-MIMIC-CXR-ablation-optimizer-muon-50ksched

EVLMs-v2 — Full model (mHC+CSA), Muon, 50k-step LR schedule (Ablation)

Ablation checkpoint of EVLMs-v2 , a medical vision-language model (Swin Transformer vision trunk + Gemma-3-1B language decoder with LoRA, multi-task heads for 14-way CheXpert classification and report generation). This repository isolates Full model (mHC+CSA), Muon, 50k-step LR schedule on the 16,000-sample MIMIC-CXR-VReason subset (777-sample official validation split), batch size 16, bfloat16 autocast, adaptive multi-task loss with per-class positive weighting.

Condition: full model, optimizer=Muon(1e-4), cosine schedule horizon 50,000 steps (stopped at 2,600) Status: Partial — stopped at the compute budget cap ( step 2,600); 2 epochs evaluated Run: https://wandb.ai/codewithdark-islamia-college-peshawar/evlms-v2/runs/1p9b0rbb

Results

Full baseline (mHC + CSA + Muon, same data budget) for reference: val loss 0.8900, accuracy 0.7515, mean AUC 0.6810 at step 6000 (see codewithdark/EVLMs-v2-MIMIC-CXR ).

Per-class metrics (last completed epoch)

Files

• final_model/model.safetensors — final language-model state dict (where the run completed)
• best_model.pt — best validation checkpoint
• checkpoints/ — periodic step checkpoints
• artifacts/epoch_XXX/ — per-epoch metrics + GradCAM explainability overlays
• config.json — tracking configuration only (secret values redacted)

Intended use & restrictions

Research use only. Trained on MIMIC-CXR-VReason (derived from MIMIC-CXR, PhysioNet) — not for clinical decision making. Credential fields in config.json are redacted by design.