Add paper_notes.md, README.md, config.json, training_args.json, model.safetensors, NOTES.md

NOTES.md

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+# Notes
+
+- check whether the config round-trips through from_pretrained
+- compare parameter counts against a reference implementation
+- move constants into the config file
+- document the expected input shapes

README.md

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+---
+license: cc0-1.0
+tags:
+- research-notes
+- data-efficient-learning
+---
+
+# Research notes: Data Efficient Learning
+
+## Overview
+
+This repository contains a working research note about **Data Efficient Learning**. It organizes motivation, related work, a falsifiable hypothesis, and an evaluation plan. It is not presented as a completed paper or a release of trained models.
+
+## Acknowledgments
+
+This repo borrows ideas freely from public reference implementations; credit to their authors.
+
+## Open questions
+
+- which baselines are actually comparable under matched capacity?
+- what is the minimum data exposure for a meaningful signal?
+- which confounders would invalidate a naive comparison?
+
+## Scope and limitations
+
+Exploratory by design: no benchmark claims, no completed ablations, no released code, no trained checkpoint. The references are a starting point for verification, not evidence that the study has run.
+
+## Contact
+
+Questions or corrections: open a discussion in this repository.
+
+## How to read this repository
+
+Read `paper_notes.md` first. Anything labeled as a plan or hypothesis is not a result. Future results should ship with dataset versions, commands, seeds, hardware and raw logs.
+
+## What is covered
+
+- research question scope and likely confounders
+- one proposed comparison against matched baselines
+- evaluation context such as task-appropriate public benchmarks named in the main note
+- reproducibility checks and open questions
+
+## Files
+
+| File | Purpose |
+|---|---|
+| `paper_notes.md` | primary artifact |
+| `README.md` | this documentation |
+| `config.json` | architecture configuration |
+| `training_args.json` | default experiment settings |
+| `model.safetensors` | initialization checkpoint |
+| `NOTES.md` | informal to-do notes |
+
+## License
+
+Distributed under the **cc0-1.0** license. External datasets keep their own terms — check them separately.

config.json

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+{
+  "architectures": [
+    "CustomResearchModel"
+  ],
+  "architecture": "transformer",
+  "model_type": "transformer",
+  "hidden_size": 256,
+  "num_hidden_layers": 8,
+  "num_attention_heads": 8,
+  "intermediate_size": 1024,
+  "hidden_act": "gelu",
+  "max_position_embeddings": 256,
+  "layer_norm_eps": 1e-12,
+  "checkpoint_status": "initialization-only",
+  "notes": "Untrained checkpoint for smoke tests; no benchmark claim.",
+  "use_cache": true,
+  "torch_dtype": "float32",
+  "vocab_size": 2048
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e3990cda25902d73cccd4fdcf05e4f1f7044f24715df75f28209540bdb83e1db
+size 132832

paper_notes.md

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+# Data Efficient Learning: Research Notes
+
+## Status
+
+Working note / experiment plan. No completed benchmark results are claimed here.
+
+## 1. Scope and motivation
+
+The goal of this note is to turn a broad idea into a falsifiable study of representation quality under limited labels, transfer, or compressed supervision. The central question is whether the proposed change improves the target behavior under a matched training and evaluation budget. The note deliberately separates hypotheses from observations so that future results can be added without rewriting the rationale.
+
+## 2. Context
+
+Research on data efficient learning often mixes improvements from architecture, data scale, preprocessing, and compute. A useful comparison therefore needs controlled baselines and explicit reporting of resource use. For this topic, the main confound is that comparisons are sensitive to pretraining data, augmentations, and tuning budgets.
+
+## 3. Working hypothesis
+
+A focused change to the representation or interaction mechanism may improve linear-probe accuracy without increasing deployment cost disproportionately. The hypothesis should be rejected if gains disappear after matching parameter count, data exposure, or tuning budget.
+
+## 4. Proposed approach
+
+The first implementation should keep modality-specific preprocessing simple, project inputs into a shared representation space, and isolate the new component behind a small interface. Baselines should include a comparable model without the component and a stronger off-the-shelf reference. Any optimization should be applied to all systems, not only the proposed one.
+
+## 5. Evaluation plan
+
+| Dataset | Role | Primary measure |
+|---|---|---|
+| ImageNet-1K | primary evaluation | linear-probe accuracy |
+| Flickr30k | transfer / robustness | Recall@1 |
+| VQAv2 | transfer / robustness | sample efficiency |
+
+Planned comparisons include a matched-capacity baseline, an ablation that removes the proposed component, and an out-of-domain transfer check. Default training values for the first controlled run are learning rate `0.0002`, batch size `48`, and `3` independent seeds. These are planning values, not claims about a finished experiment.
+
+## 6. Reproducibility checklist
+
+- Separate model selection from final evaluation.
+- Run at least one out-of-domain test.
+- Track failed runs as well as successful runs.
+- Document every exclusion rule.
+
+## 7. Failure modes and responsible use
+
+The analysis should report subgroup and category-level failures instead of relying only on a single aggregate score. Particular attention is needed because comparisons are sensitive to pretraining data, augmentations, and tuning budgets. No production use is recommended without task-specific validation, data review, and an assessment of privacy and bias.
+
+## 8. Open questions
+
+- Where does the method fail on compositional or out-of-domain examples?
+- Which gain survives when the compute budget is matched?
+- Can a simpler baseline recover the same gain with more careful tuning?
+
+## References
+
+[1] Chen et al., SimCLR, 2020.
+[2] He et al., MAE, 2022.
+[3] Hinton et al., Knowledge Distillation, 2015.

training_args.json

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+{
+  "optimizer": "adamw",
+  "scheduler": "cosine",
+  "learning_rate": 5e-05,
+  "batch_size": 16,
+  "epochs": 10,
+  "seed": 123,
+  "status": "default recipe; not a completed run"
+}