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README.md

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+# Cross-Model LoRA Adapter Prediction
+
+Zero-shot prediction of a LoRA adapter for **Model Y on a held-out task D**, using only:
+- 4 LoRAs trained on Model **X** (X_A, X_B, X_C, X_D)
+- 3 LoRAs trained on Model **Y** (Y_A, Y_B, Y_C — Y_D never trained for the prediction)
+
+A small mapping `f` is learned from the 3 paired anchor adapters
+`(X_A↔Y_A, X_B↔Y_B, X_C↔Y_C)` and applied to `X_D` to predict `Ŷ_D = f(X_D)`.
+
+Inspired by Sakana AI's **Text-to-LoRA** hypernetwork (arXiv 2506.06105) and related cross-base-model
+adapter transfer work (Trans-LoRA, arXiv 2405.17258). T2L uses a *text*-conditioned hypernetwork to
+generate LoRA weights; here we replace the text condition with the *paired adapter on another model*
+and use a closed-form anchor-basis ridge regression instead of a neural hypernetwork (because we only
+have 3 anchor pairs).
+
+## Setup
+
+| | |
+|---|---|
+| Model X | `Qwen/Qwen2.5-0.5B-Instruct` (hidden=896, 24 layers) |
+| Model Y | `meta-llama/Llama-3.2-1B-Instruct` (hidden=2048, 16 layers) |
+| LoRA | r=8, α=16, target modules = q_proj, v_proj |
+| Tasks | A=SST-2, B=AG News, C=SetFit/subj, D=dair-ai/emotion (held out for Y in the prediction setup; Y_D is also trained as an oracle for comparison only) |
+| Train per task | 1500 SFT examples, 1 epoch, bs=8, lr=2e-4, bf16 |
+| Eval | 400 examples, greedy generation, label-prefix matching |
+
+## Mapping function `f`
+
+Adapters are flattened to vectors. We have only **3 paired samples**, so a full per-parameter linear
+map (`Y_dim × X_dim` ≈ 460M params) is hopelessly under-determined. Instead we use the closed-form
+**anchor-basis ridge mapping**:
+
+1. Center: `X_c[i] = X_i − mean(X)`, `Y_c[i] = Y_i − mean(Y)` for `i ∈ {A,B,C}`.
+2. Solve `(X_cᵀX_c + λI) α = X_cᵀ (X_D − mean(X))` → 3-dim coefficients α (3×3 system).
+3. Predict `Ŷ_D = mean(Y) + α · Y_c`.
+
+This expresses the predicted Y-adapter as `mean(Y) + linear combination of Y-side anchor offsets`,
+where the combination weights are picked to best reproduce `X_D − mean(X)` from the X-side anchor
+offsets. Equivalent to fitting a linear map restricted to the 3-dim subspace spanned by the centred
+X-anchors. Ridge λ = 1e-3.
+
+## Results — Accuracy on task D (Emotion, 400 eval examples)
+
+| Setting | Accuracy |
+|---|---:|
+| base Model Y (no adapter) | 0.308 |
+| Y + Y_A (wrong-task adapter) | 0.510 |
+| Y + Y_B (wrong-task adapter) | 0.538 |
+| Y + Y_C (wrong-task adapter) | 0.470 |
+| Y + **mean(Y_A,Y_B,Y_C)** baseline | 0.505 |
+| Y + **Ŷ_D = f(X_D)** ← predicted, never trained on D for Y | **0.520** |
+| Y + Y_D (oracle, actually trained on D) | 0.665 |
+| base Model X (no adapter) | 0.285 |
+| X + X_D (oracle on Model X) | 0.608 |
+
+**Cosine similarity of full adapter vectors to the oracle Y_D:**
+
+| | cos to Y_D |
+|---|---:|
+| Y_A | 0.947 |
+| Y_B | 0.927 |
+| Y_C | 0.942 |
+| mean(Y_A,Y_B,Y_C) | 0.957 |
+| **Ŷ_D = f(X_D)** | **0.951** |
+
+α coefficients on (A,B,C): `[-0.429, -0.009, 0.119]` (so the prediction is dominated by `mean(Y)`
+plus a small negative pull away from the SST-2 anchor).
+
+## Verdict
+
+The predicted adapter:
+- **Works**: jumps from 0.308 (no adapter) to 0.520 (predicted), recovering ~59% of the gap to the
+  fully-trained oracle (0.665).
+- **Marginally beats** the naive "mean of known Y-adapters" baseline (0.520 vs 0.505, +1.5 pts).
+- Is essentially a near-mean of the anchors in adapter space (cos≈0.95), which is what one should
+  expect with only 3 anchor pairs and a hugely over-parameterised target. Ridge regularization
+  collapses most of the prediction toward the anchor mean.
+
+**Implication.** Your zero-shot cross-model adapter prediction idea is *directionally validated*:
+information from `X_D` is being transferred and improves over the mean baseline. To make the
+prediction substantially better than "average your known Y adapters", you need either (a) many more
+paired anchors so a real hypernetwork (à la T2L) can learn the X→Y map, or (b) structural priors
+exploiting the LoRA factorisation per layer (e.g. Procrustes per matrix, or shared low-rank basis
+across tasks).
+
+## Files in this repo
+
+```
+X/X_A,X_B,X_C,X_D/         # PEFT LoRA adapters trained on Qwen2.5-0.5B-Instruct
+Y/Y_A,Y_B,Y_C,Y_D/         # PEFT LoRA adapters trained on Llama-3.2-1B-Instruct
+Y/Y_pred_D/                # PREDICTED adapter Ŷ_D = f(X_D), drop-in PEFT adapter for Y
+Y/Y_mean_ABC/              # Mean-of-anchors baseline adapter
+results.json               # Final accuracies
+mapping_diagnostics.json   # alpha coefficients, cosine sims, dims
+pipeline.py                # End-to-end training/mapping/eval script
+run.log                    # Full training log
+```
+
+## Reproduce
+
+```bash
+pip install torch transformers==4.46.3 peft==0.13.2 trl==0.12.1 datasets==3.1.0 accelerate==1.1.1
+python pipeline.py --stage all      # ~10 min on a single A10G
+```
+
+## Use the predicted adapter
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+base = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct", torch_dtype=torch.bfloat16)
+tok  = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B-Instruct")
+model = PeftModel.from_pretrained(base, "Samarth0710/cross-model-lora-prediction", subfolder="Y/Y_pred_D")
+```
+
+## References
+- Sakana AI, *Text-to-LoRA: Instant Transformer Adaptation* — arXiv 2506.06105
+- *Trans-LoRA: Towards Data-Free Transferable Parameter-Efficient Finetuning* — arXiv 2405.17258