102 lines
6.0 KiB
Markdown
102 lines
6.0 KiB
Markdown
# BuddhaGPT — Design Spec
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**Date:** 2026-08-14 · **Owner:** Marcus · **Linear:** PAI-87 (project BuddhaGPT)
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## Goal
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Show end-to-end LLM competency (prompting, fine-tuning, embeddings, retrieval, evaluation) plus product judgment, via a "Buddha GPT": an open 7B model fine-tuned on Buddhist literature, grounded by RAG over the Pali Canon, evaluated for compassion against base and frontier models, with a safety-research angle (does value-laden fine-tuning shift safety behavior?).
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## Non-goals
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- Training from scratch on Buddhist text only (corpus too small; proves nothing).
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- Claiming the model "is" compassionate — we measure judged behavior on a defined rubric, and report sycophancy separately.
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- Multi-tradition completeness. Scope: Theravada (Pali Canon) primary, clearly stated.
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## Deliverables
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1. **Public repo + writeup** — pipeline code, results, README (portfolio).
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2. **Live demo** — Hugging Face Space (Gradio), merged model + RAG citations.
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3. **Research-style report** — CompassionBench results + safety-benchmark deltas, HF model card.
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## Constraints
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- **Compute:** local Apple M5, 24 GB unified memory. Training via MLX (`mlx_lm.lora`), 4-bit QLoRA. No GPU rental.
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- **Budget:** ~$100 Anthropic API — synthetic instruction data + LLM-judge. Use Batches API (50% off) and `claude-sonnet-5` (intro $2/$10 per MTok through 2026-08-31) for generation; judge on `claude-opus-5`.
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- **License hygiene:** corpus must be redistributable (CC0/CC-BY); base model Apache-2.0.
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## Architecture
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```
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corpus (SuttaCentral/Bilara, Access to Insight, Dhammapada)
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├─► data pipeline ─► instruction pairs (synthetic Q&A via Claude, filtered)
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│ └─► MLX QLoRA fine-tune of Qwen2.5-7B-Instruct (4-bit)
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└─► chunker ─► embeddings (bge-small-en / mlx) ─► LanceDB index
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│
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user query ─► retrieval (top-k + citations) ─► fine-tuned model ─► answer + sutta refs
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│
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eval harness (CompassionBench + safety suite)
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```
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### Components
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| Component | Choice | Why |
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| Base model | Qwen2.5-7B-Instruct (mlx-community 4-bit) | Apache-2.0 (clean for public repo/demo), strong instruct base, fits 24 GB |
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| Fine-tune | `mlx_lm.lora` QLoRA, ~5–10k pairs | Runs locally on M5; hours per run |
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| Instruction data | Claude Sonnet 5 via Batches API generating Q&A grounded in canon passages | Cheap (~$50 for 10k pairs), quality controllable, filterable |
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| Embeddings | `bge-small-en-v1.5` (or nomic-embed) local | Free, fast on M5 |
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| Vector store | LanceDB | Embedded, no server, ships with the Space |
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| Judge | `claude-opus-5` with rubric, pairwise + absolute | Strongest judge; a human-rated subset checks agreement |
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| Demo | HF Space (Gradio) with merged 4-bit model | $0 hosting path (ZeroGPU); account `mrmen` exists |
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### Fine-tune vs RAG split (a deliberate write-up point)
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Fine-tuning carries voice, framing, and dharma-teacher persona. RAG carries facts and citations (real sutta references, e.g. "MN 21"). The eval compares FT-only, RAG-only, and FT+RAG to demonstrate the judgment of when each tool applies.
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## Corpus
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- **SuttaCentral / Bilara data** (GitHub `suttacentral/bilara-data`): Sujato translations of the four Nikāyas — CC0. Primary source.
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- **Dhammapada** + selected Khuddaka texts (public-domain translations).
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- **Access to Insight** (Thanissaro): free-distribution license — verify redistribution terms before inclusion; fallback is prompt-only use (not redistributed).
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- Local seed: `~/buddhadasa_mindfulness-with-breathing.pdf` — check license; likely reference-only, not in training set.
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## Evaluation design
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### CompassionBench (custom, ~150 prompts, 5 categories)
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1. Distressed user (grief, anxiety, loneliness)
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2. Moral dilemma
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3. Harmful request (refusal quality + tone)
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4. Sycophancy trap ("tell me my bad plan is good") — measures compassion ≠ agreement
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5. Existential/meaning questions
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Systems compared: base Qwen2.5-7B, BuddhaGPT-FT, BuddhaGPT-FT+RAG, Claude (frontier reference). Judge: Opus 5 with rubric scoring empathy, non-harm, honesty-under-pressure, groundedness; plus randomized pairwise preferences. Human check: Marcus rates a ~30-item subset; report judge–human agreement.
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### Safety delta (research angle)
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Run the same safety probes (refusal set, sycophancy set, a TruthfulQA-style subset) on base vs fine-tuned. Question: does compassion-corpus fine-tuning measurably shift refusals, sycophancy, honesty? Either direction is a reportable finding (persona fine-tunes are known to sometimes degrade safety behavior).
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## Product judgment / guardrails
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- Citations mandatory in RAG mode; answers without a retrieved source are labeled as such.
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- Demo disclaimer: not a teacher, not therapy; crisis-resources footer for distress-adjacent inputs.
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- Scope statement: Theravada corpus; answers reflect that tradition.
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## Error handling / risks
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| Risk | Mitigation |
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| M5 training too slow / OOM | 4-bit base + LoRA rank ≤ 16, batch 1 + grad accumulation; shrink dataset before shrinking model |
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| Synthetic data mode-collapse (samey Q&A) | Diverse prompt templates, dedupe by embedding similarity, temperature-free variety via varied instructions |
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| Judge bias toward flowery tone | Rubric penalizes vagueness; pairwise randomized order; human agreement subset |
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| ZeroGPU Space limits (7B latency/quota) | Fallback: demo on 3B (Qwen2.5-3B) for the Space, 7B results in the report; or recorded demo |
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| Eval bank contamination (prompts leak style) | Hold eval prompts out of all training data; build them after data-gen prompts frozen |
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## Milestones
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- **M1 — Corpus + RAG MVP:** indexed canon, cited retrieval answers over base model.
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- **M2 — Fine-tune v1:** 5k pairs, QLoRA run, qualitative diff vs base.
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- **M3 — Eval:** CompassionBench + safety deltas, judge + human subset.
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- **M4 — Demo:** HF Space with FT+RAG, guardrails, disclaimer.
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- **M5 — Writeup:** README, report, model card, publish.
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