The A-Lora Method
PSA: You use the word 'methodology' which is the incorrect word.
"The A-LoRA Methodology
A-LoRA (Atom LoRA) is a fine-tuning methodology "
The correct term is "method". Please learn the distinction and use it correctly.
I am curious how your method generates relationship data between the individual atoms. Is there an implementation of this method that we can inspect?
Thank you.
Hi Dan,
I'll share the complete test results with detailed configurations — including what I tried with different atom component arrangements — and upload a link here so you can review.
To answer your question directly: each atom is a single piece of a single teacher's actual teaching, decomposed into the parts described in the README (transformation before/after/how, concept relations, anchoring quotes, teacher method).
The upstream pipeline is local — OCR-based PDF parsing of the source books, then a multi-step extraction workflow that decomposes each teaching passage into these structured atom components first (step 1).
The training format is V6: question + concept arrows go in as input. The output is a multi-step teaching passage — anchoring quotes, transformation (before/after), and method — all preserved as one complete teaching move( Step 2). The concept arrows act as a reasoning signal telling the model what conceptual movement to make.
I've also tried other configurations — sending components separately, different combinations, multi(4)-ChromaDB RAG setups. Each gave very different results in "my local chat with the atoms" step. I'll try to share examples later.
What's interesting is that the model captures how these teachers thought and approached problems — not just surface style. It produces original synthesis, generating new teaching moves using absorbed reasoning patterns rather than reproducing memorised text.
My goal is to scale this to larger models (70B+) and see what original synthesis emerges when atoms from multiple fields are combined.
Thanks for the correction on method vs methodology — noted.
Thanks, but don't feel obliged to!
I am very curious though - it sounds very much like something I have wanted to do! Seems like proper 'framing' of chunks should be able to get better associations than raw text. I never researched it far though.
So your work as-is is appreciated :) Anything in addition that you can share will be studied!
I believe knowledge should be shared openly rather than kept hidden for personal benefit. I have uploaded a PDF that outlines the approach, results, and overall methodology. Please take a look, and let me know if you have any questions.