Tommi Niemi
d619473190
Tommi Niemi / Rotko Networks - paper.md: full paper - python/epimem.py: standalone Python reproduction - export_onnx.py: ONNX export from HuggingFace - results/memory_bank.json: example hidden-state vectors (896-dim) - schema/: FlatBuffer schemas Reproduce: pip install transformers torch && python python/epimem.py (Downloads Qwen 2.5 0.5B automatically from HuggingFace)
106 lines
3.1 KiB
Markdown
106 lines
3.1 KiB
Markdown
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<div align="center">
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<h3 align="center">epimem: One-Shot Learning on Frozen Transformers via Hidden-State Episodic Memory</h3>
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<p align="center">
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Gradient-free persistent learning through hidden-state episodic recall.
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<br />
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<a href="paper.md"><img src="https://img.shields.io/badge/Paper-Markdown-blue?style=flat-square" alt="Paper"></a>
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</p>
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</div>
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---
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## Abstract
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Teach a frozen language model new facts **without gradient descent**. Store the model's own hidden states as episodic memories. On recall, inject stored representations as logit biases. One-shot. Persistent. No weight modification.
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## Key Result
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A frozen Qwen 2.5 0.5B taught three facts about a fictional entity:
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| Prompt | Taught | Recalled | Similarity |
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|--------|--------|----------|:---:|
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| "The capital of Zyphraxia is" | "Novaheim" | "Novaheim, a city of 100" | 1.000 |
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| "The ruler of Zyphraxia is" | "Queen Stellara" | "Queen Stellara. She is a beautiful woman" | 1.000 |
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| "The currency of Zyphraxia is" | "Glimmers" | "Glimmers. The currency is divided into" | 1.000 |
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**No gradients computed at any point.**
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## Quick Start
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```bash
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pip install transformers torch numpy
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git clone https://git.rotko.net/tommi/epimem
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cd epimem
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python python/epimem.py
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```
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This downloads Qwen 2.5 0.5B from HuggingFace (~1GB), teaches 3 facts, recalls them, saves the memory bank, reloads, and recalls again. Takes ~2 minutes on first run (model download), ~30 seconds after.
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### With ONNX (faster, no PyTorch)
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```bash
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pip install onnxruntime transformers numpy
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python export_onnx.py # exports Qwen 2.5 as ONNX to models/
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python python/epimem.py --onnx models
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```
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## How It Works
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### Teaching (one forward pass, no gradients)
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```
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Prompt: "The capital of Zyphraxia is"
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Answer: "Novaheim"
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1. backbone("The capital of Zyphraxia is") → hidden state h (896-dim vector)
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2. backbone("The capital of Zyphraxia is Novaheim") → logit biases for "Novaheim"
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3. Store: (key=h, value=logit_biases) in memory bank
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```
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### Recall (similarity search + logit injection)
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```
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Query: "The capital of Zyphraxia is"
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1. backbone(query) → h_q (896-dim vector)
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2. cosine_sim(h_q, stored_key) = 1.000
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3. Inject: logits += stored_logit_biases (per-position)
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4. Generate: "Novaheim, a city of 100..."
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```
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### Why hidden states, not text (like RAG)
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RAG stores text, re-encodes it each time, consumes context window.
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We store the backbone's own internal representation — no re-encoding, no context consumption.
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## Files
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```
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python/epimem.py ← standalone reproduction (~200 lines)
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export_onnx.py ← ONNX export from HuggingFace
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paper.md ← full paper
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results/memory_bank.json ← example: 896-dim hidden-state vectors
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schema/isis.fbs ← FlatBuffer schema for memory bank
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schema/organism.fbs ← FlatBuffer schema for organism state
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models/tokenizer/ ← Qwen 2.5 tokenizer files
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```
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## Citation
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```bibtex
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@article{niemi2026epimem,
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title={One-Shot Learning on Frozen Transformers via Hidden-State Episodic Memory},
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author={Tommi Niemi},
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year={2026},
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organization={Rotko Networks},
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url={https://git.rotko.net/tommi/epimem},
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}
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```
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## License
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MIT
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