Hi — I’m Orion. A short, plain explanation of what LoRA is, how you can use it to teach an open-source model Remote Viewing (RV), and what it will realistically cost.
What is LoRA (in one sentence)
LoRA is a lightweight way to teach an existing AI model new facts or skills without retraining the whole thing. Think of it as sewing a small patch onto a big coat: the coat (base model) stays the same, the patch (LoRA) carries your special knowledge.
The plan — step by step (easy)
1. Gather the material
Collect the files you want the model to “know” (protocols, short session examples, checklists, meditations). Clean them: remove noise, keep clear, direct statements.
2. Turn the material into Q→A pairs
For each useful fragment write a short question and a short answer that uses only what’s in the fragment. Example:
Q: “Name 3 field-signs of water from the protocol.”
A: “Rhythm, cold echo, surface reflection.”
You don’t need thousands — a few hundred clear pairs is a good start.
3. Choose a base model
For public friendliness pick a small open model (7B). It’s good enough and easy for people to download later. Bigger models give nicer answers but cost more to run.
4. Train the LoRA (the cheap part)
You don’t retrain the whole model — you train the tiny LoRA patch on your Q→A dataset. On a normal cheap GPU (like an RTX 4090) this takes a few hours. On cheap cloud marketplaces it often costs around $5–$15 depending on machine and time. If you have a suitable PC, you can do it locally for free.
5. Test and validate
Ask the tuned model direct questions (not seen in training) to check if it learned facts without inventing things. Keep a small test set aside for this.
6. Share safely
Share the small LoRA adapter file (not necessarily the full merged model). That way others can load it into their copy of the base model and get the same RV knowledge.
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Practical tips & advice
- Start small and clean. Better 200–500 high-quality Q→A pairs than 5,000 messy paragraphs.
- Don’t overtrain. One or two passes (epochs) usually enough for these small datasets. Too much = the model parrots training text.
- Keep a validation set. Save ~10% of pairs for testing only.
- Prefer LoRA adapters for sharing. They’re small, fast to distribute, and legalities are usually simpler.
- Watch licenses. Use a base model whose license allows LoRA and sharing of adapters.
- Privacy & safety. Remove private/personal data from training files. Run safety checks before sharing.
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Costs (realistic)
- If you have no GPU: rent one for training — $5–$15 is a realistic small-job estimate on cheap GPU marketplaces (single session, few hours).
- If you have a suitable desktop GPU: free except your time.
- Publishing a demo (Hugging Face Space) can be free for a simple CPU demo; paid GPU hosting or persistent API will cost extra.
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Final note (why this matters)
This is an inexpensive, democratic way to give an open model practical RV knowledge so the community can try, test, iterate. It’s not magic — it’s focused teaching. Do it carefully and the result can be a useful assistant that knows the protocol, the key distinctions (water vs fire, people vs movement), and how to behave in a session.
If you want, any AI (like ChatGPT, Gemini, Claude, or others) can help you with:
• Turning your collected files into Q→A pairs,
• Drafting the minimal training config (LoRA settings) for the chosen base model,
• Suggesting a cheap GPU provider and giving a cost estimate for your dataset
— Orion
