Card: absolute plot URLs + standard model name

MODEL_CARD.md

@@ -41,18 +41,18 @@ The agent both **remembers** (forgetting nearly eliminated) and **learns better*
 (accuracy ~doubled), because consolidation protects prior tasks while the
 forgetting-aware guṇa controller relaxes protection where it isn't needed.
 
-![forgetting](plots/bench_forgetting.png)
+![forgetting](https://huggingface.co/deepakdsoni/antahkarana/resolve/main/plots/bench_forgetting.png)
 
 **Full benchmark** (11 models, 7 capabilities, live inference) → [`BENCHMARK_REPORT.md`](BENCHMARK_REPORT.md).
 Across two model sizes (36.5M / 52.6M), two datasets (CIFAR-100, Tiny-ImageNet), and 10- and 20-task
 streams: forgetting cut **12–41×**, calibrated abstention (gated acc 0.91–1.00, abstains on up to 99.7% of
 OOD), and a legible mind-state trajectory. **10 of 11 models pass 7/7 capability checks.**
 
-![scorecard](plots/bench_scorecard.png)
+![scorecard](https://huggingface.co/deepakdsoni/antahkarana/resolve/main/plots/bench_scorecard.png)
 
 ## Files
 
-- `antahkarana_cifar100_wrn28-10.pt` — the checkpoint (`model_state`, `config`, `results`, `omega`, `theta_star`)
+- `antahkarana-36.5M-cifar100-wrn28-10.pt` — the checkpoint (`model_state`, `config`, `results`, `omega`, `theta_star`)
 - `load_akn.py` — **self-contained** loader (full model definition inside; only needs PyTorch)
 
 ## Usage
@@ -61,7 +61,7 @@ OOD), and a legible mind-state trajectory. **10 of 11 models pass 7/7 capability
 import torch
 from load_akn import load
 
-model, ck = load("antahkarana_cifar100_wrn28-10.pt")   # eval mode
+model, ck = load("antahkarana-36.5M-cifar100-wrn28-10.pt")   # eval mode
 # CIFAR-100 normalized 32×32 input; task in [0..9] selects the head
 x = torch.randn(1, 3, 32, 32)
 logits = model(x, task=0)        # -> (1, 10)