Create README.md

README.md

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+---
+library_name: stacklm
+license: apache-2.0
+tags:
+  - stacklm
+  - multi-task
+  - lora-composition
+  - query-time-fit
+  - unlearning
+---
+
+# stacklm-tiny
+
+A tiny transformer (~11K parameters) demonstrating **additive stack
+composition** for multi-task language modeling.
+
+## Architecture
+
+Frozen base transformer + N additive residual stacks on output logits.
+No router parameters. Alpha (stack mixing weights) is fit at query time
+on a small labeled example set.
+
+    f(x) = base(x) + sum_i alpha_i * stack_i(x)
+
+## Validated claims
+
+Tested locally, synthetic tasks, seed 0:
+
+| Claim | Result | Baseline |
+|---|---|---|
+| Query-fit alpha ~ oracle | ratio 1.003 | 20 labeled examples |
+| Query-fit vs softmax router | ratio 0.926 | Trained router |
+| Anti-stack cancellation | 97% exact | log-space ratio 0.030 |
+| Composition linearity | 98% exact | [1,1] vs [2,0] |
+| Per-sample alpha beats joint | ratio 0.892 | 11% improvement |
+
+## Usage
+
+    from stacklm_tiny import StackLM, StackLMConfig, TrainConfig
+
+    model = StackLM.from_pretrained('./stacklm-tiny')
+    alpha = model.fit_alpha_joint(X_adapt, Y_adapt, model.n_active, tcfg)
+    logits = model(X_test, alpha=alpha)
+
+## Revocation
+
+    anti_idx = model.train_anti_stack(task, target_idx=0, tcfg=tcfg)
+    alpha = torch.tensor([1., 1.])
+    out = model(X, alpha=alpha, n=2)
+
+## What this is / is not
+
+**Is:** a proof-of-concept demonstrating that (a) multi-task can be
+additive rather than routed, (b) mixing weights are optimally fitted
+at query time, (c) adapters can be partially revoked by adding a
+cancellation stack.
+
+**Is not:** a useful language model. It is a demonstration model.
+For real use cases, the same architecture applies to LoRA stacks on
+a real base.
+
+## Files
+
+- pytorch_model.bin -- base + stacks weights
+- config.json -- architecture config
+- stacklm_tiny.py -- model code