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README.md

@@ -73,55 +73,74 @@ print(output.logits.shape)
 print(output.last_hidden_state.shape)
 ```
 
-Pass `output_hidden_states=True` if you need all intermediate hidden states.
-
-## Binder Design Regularizer
-
-The FastPLMs binder design tutorial uses `Synthyra/ESMplusplus_6B` as the
-ESMC-style masked-LM regularizer while FastPLMs ESMFold2 experimental models
-provide differentiable folding losses and final critics. The script lives at
-`cookbook/tutorials/binder_design_fastplms.py` and supports local CUDA Docker
-runs plus Modal deployment.
-
-Run the verified EGFR 128 amino acid de novo minibinder example:
-
-```bash
-cd /home/ubuntu/FastPLMs
-
-sudo -n docker run --gpus all --rm \
-  -v /home/ubuntu/FastPLMs:/app \
-  -v /home/ubuntu/FastPLMs:/workspace \
-  -v /home/ubuntu/.cache/huggingface:/workspace/.cache/huggingface \
-  -w /workspace fastplms-esmfold2 \
-  python /app/cookbook/tutorials/binder_design_fastplms.py \
-    --backend local \
-    --target-name egfr \
-    --binder-sequence '################################################################################################################################' \
-    --not-antibody \
-    --steps 150 \
-    --batch-size 1 \
-    --seed 103 \
-    --output-dir /workspace/campaign_egfr_len128_b1_s150_seed103_consensus_cli
-```
-
-The run writes `trajectory.jsonl`, `best_sequences.fasta`, `results.parquet`,
-`selection.parquet`, and per-critic PDB/CIF/logit files. The verified candidate
-had hero mean iPTM `0.913870`, hero min iPTM `0.904600`, and all four ESMFold2
-hero critics above `0.9`.
-
-Binder sequence:
-
-```text
-SAVKHLLEIVKYLEEAIEKALEVDPVFLVPPAAEELLIAAKVIKELAKENPELIEVYELLMKAVKGLKKLVRSNDKEILREVIRLLRKAAKVIREILKNNPDLDPELRKALEELAKVLEEIAEVLEQQ
-```
-
-See [`docs/binder_design.md`](https://github.com/Synthyra/FastPLMs/blob/main/docs/binder_design.md)
-for the full strategy, Modal backend, official pI and selection scoring,
-per-critic metrics, and caveats.
-
-## Embed Datasets
-
-All FastPLMs sequence models include `embed_dataset`, which handles batching, length sorting, pooling, FASTA parsing, optional resume from existing outputs, and `.pth` or SQLite storage.
+Pass `output_hidden_states=True` if you need all intermediate hidden states.
+
+## Experimental Test-Time Training
+
+TTT is disabled by default. Normal ESM++ inference, embeddings, logits, and
+`state_dict()` keys are unchanged unless you explicitly call `model.ttt(...)`.
+The current implementation is experimental and trains only local LoRA adapters
+on the ESMC backbone with masked language modeling on the test protein. It can
+help some difficult proteins, but it adds test-time compute and can degrade
+already confident predictions. The 6B checkpoint is large, so start with small
+`steps`, `ags`, and `batch_size` values.
+
+```python
+metrics = model.ttt(
+    seq="MSTNPKPQRKTKRNT",
+    ttt_config={"steps": 1, "ags": 1, "batch_size": 1},
+)
+model.ttt_reset()
+print(metrics["losses"])
+```
+
+## Binder Design Regularizer
+
+The FastPLMs binder design tutorial uses `Synthyra/ESMplusplus_6B` as the
+ESMC-style masked-LM regularizer while FastPLMs ESMFold2 experimental models
+provide differentiable folding losses and final critics. The script lives at
+`cookbook/tutorials/binder_design_fastplms.py` and supports local CUDA Docker
+runs plus Modal deployment.
+
+Run the verified EGFR 128 amino acid de novo minibinder example:
+
+```bash
+cd /home/ubuntu/FastPLMs
+
+sudo -n docker run --gpus all --rm \
+  -v /home/ubuntu/FastPLMs:/app \
+  -v /home/ubuntu/FastPLMs:/workspace \
+  -v /home/ubuntu/.cache/huggingface:/workspace/.cache/huggingface \
+  -w /workspace fastplms-esmfold2 \
+  python /app/cookbook/tutorials/binder_design_fastplms.py \
+    --backend local \
+    --target-name egfr \
+    --binder-sequence '################################################################################################################################' \
+    --not-antibody \
+    --steps 150 \
+    --batch-size 1 \
+    --seed 103 \
+    --output-dir /workspace/campaign_egfr_len128_b1_s150_seed103_consensus_cli
+```
+
+The run writes `trajectory.jsonl`, `best_sequences.fasta`, `results.parquet`,
+`selection.parquet`, and per-critic PDB/CIF/logit files. The verified candidate
+had hero mean iPTM `0.913870`, hero min iPTM `0.904600`, and all four ESMFold2
+hero critics above `0.9`.
+
+Binder sequence:
+
+```text
+SAVKHLLEIVKYLEEAIEKALEVDPVFLVPPAAEELLIAAKVIKELAKENPELIEVYELLMKAVKGLKKLVRSNDKEILREVIRLLRKAAKVIREILKNNPDLDPELRKALEELAKVLEEIAEVLEQQ
+```
+
+See [`docs/binder_design.md`](https://github.com/Synthyra/FastPLMs/blob/main/docs/binder_design.md)
+for the full strategy, Modal backend, official pI and selection scoring,
+per-critic metrics, and caveats.
+
+## Embed Datasets
+
+All FastPLMs sequence models include `embed_dataset`, which handles batching, length sorting, pooling, FASTA parsing, optional resume from existing outputs, and `.pth` or SQLite storage.
 
 ```python
 import torch