fix: complete template coverage in training data + disclose model alias

README.md

@@ -0,0 +1,22 @@
+# BuddhaGPT
+
+Fine-tune + RAG experiment: a small local model trained and grounded on the Pali Canon (via
+[SuttaCentral](https://suttacentral.net)'s Bilara texts).
+
+## Data generation
+
+Synthetic instruction pairs (`data/instructions.jsonl`) are generated from `corpus/suttas.jsonl`
+via `scripts/gen_data.py`, using OpenRouter model `~deepseek/deepseek-v4-flash-latest` (the
+tilde prefix is part of OpenRouter's real catalog ID for this "latest" alias — verified against
+the live `/api/v1/models` catalog, not a typo).
+
+Token usage / cost:
+
+- Original full run (`--mode full`, 3,200 calls, variants 0–1 only): totals were not persisted
+  and the generating process died before a report was written, so these figures are an
+  **estimate**, not measured: ~5.2M input / 1.8M output tokens, ≈$0.4–0.7 at list pricing
+  (~$0.08/M in, $0.16/M out).
+- Template top-up run (`--mode topup`, 2,300 calls, variants 2–5, 0 failures): **measured** —
+  1,994,673 input tokens / 1,618,867 output tokens, **$0.42** at list pricing (~$0.08/M in,
+  $0.16/M out). See `.superpowers/sdd/2026-08-14-buddha-gpt/task-5-report.md` for the full
+  fix-round report, including per-template pair counts.

scripts/gen_data.py

@@ -1,30 +1,48 @@
-import json, random, time, threading
+import argparse, json, random, time, threading
 from concurrent.futures import ThreadPoolExecutor
 from pathlib import Path
 from buddhagpt.llm import openrouter_client, chat
-from buddhagpt.datagen import build_messages, parse_pairs, dedupe
+from buddhagpt.datagen import build_messages, parse_pairs, dedupe, interleaved_variant
 
 # NOTE: OpenRouter lists this model under the tilde-prefixed "latest" alias id.
 MODEL = "~deepseek/deepseek-v4-flash-latest"
+
+
+def load_qualifying() -> list[dict]:
     suttas = [json.loads(l) for l in Path("corpus/suttas.jsonl").read_text().splitlines()]
-random.seed(7)
-qualifying = [s for s in suttas if len(s["text"]) > 800]  # only 2304 suttas qualify (corpus is
-                                                            # smaller than assumed) — cycle through
-                                                            # them with rotating template variants
-                                                            # to reach the target call volume instead
-                                                            # of random.sample()'ing more than exist.
-TARGET_CALLS = 3200
-sample = []  # list of (sutta, variant) tuples
-pass_num = 0
-while len(sample) < TARGET_CALLS:
+    return [s for s in suttas if len(s["text"]) > 800]  # only 2304 of 3920 suttas qualify
+
+
+def build_full_sample(qualifying: list[dict], target_calls: int, seed: int) -> list[tuple[dict, int]]:
+    """Cycle through the qualifying corpus (reshuffled each pass) until target_calls is
+    reached, assigning template variant by running call index so every full run covers
+    all templates in TEMPLATES rather than one variant per pass."""
+    random.seed(seed)
+    sample = []
+    while len(sample) < target_calls:
         order = qualifying[:]
         random.shuffle(order)
         for s in order:
-        if len(sample) >= TARGET_CALLS:
+            if len(sample) >= target_calls:
                 break
-        sample.append((s, pass_num))
-    pass_num += 1
+            sample.append((s, interleaved_variant(len(sample))))
+    return sample
 
+
+def build_topup_sample(qualifying: list[dict], variants: list[int], per_variant: int, seed: int) -> list[tuple[dict, int]]:
+    """Sample `per_variant` distinct suttas (seeded, no repeats within a variant) for each
+    variant in `variants`, for topping up underrepresented templates."""
+    sample = []
+    for variant in variants:
+        random.seed(seed + variant)
+        chosen = random.sample(qualifying, min(per_variant, len(qualifying)))
+        sample += [(s, variant) for s in chosen]
+    random.seed(seed)
+    random.shuffle(sample)
+    return sample
+
+
+def run(sample: list[tuple[dict, int]], raw_path: Path) -> tuple[dict, int, int]:
     client = openrouter_client()
     totals = {"input": 0, "output": 0}
     fail_count = 0
@@ -34,11 +52,10 @@ start = time.time()
     lock = threading.Lock()
 
     Path("data").mkdir(parents=True, exist_ok=True)
-raw_path = Path("data/instructions_raw.jsonl")
     raw_f = raw_path.open("a")  # append: incremental persistence, survives interruption
 
     def gen_one(args):
-    global fail_count, done_count, raw_count
+        nonlocal fail_count, done_count, raw_count
         s, variant = args
         try:
             text, usage = chat(client, MODEL, build_messages(s, variant), max_tokens=2000)
@@ -48,6 +65,8 @@ def gen_one(args):
             print(f"skip {s['uid']}: {e}", flush=True)
             return
         pairs = parse_pairs(text, uid=s["uid"])
+        for p in pairs:
+            p["variant"] = variant
         with lock:
             totals["input"] += usage["input"]; totals["output"] += usage["output"]
             done_count += 1
@@ -65,18 +84,54 @@ with ThreadPoolExecutor(max_workers=8) as pool:
 
     raw_f.close()
 
-fail_rate = fail_count / len(sample)
+    fail_rate = fail_count / len(sample) if sample else 0.0
     print(f"calls: {len(sample)} | failed: {fail_count} ({fail_rate:.1%}) | raw pairs parsed: {raw_count}", flush=True)
+    return totals, fail_count, raw_count
 
+
+def rebuild_instructions(raw_path: Path, out_path: Path) -> list[dict]:
+    """Rebuild data/instructions.jsonl from the FULL raw file (word filter + dedupe)."""
     pairs = [json.loads(l) for l in raw_path.read_text().splitlines() if l.strip()]
     pairs = [p for p in pairs if 60 <= len(p["answer"].split()) <= 400]
     print(f"pairs after word-count filter: {len(pairs)}", flush=True)
     pairs = dedupe(pairs)
-with Path("data/instructions.jsonl").open("w") as f:
+    with out_path.open("w") as f:
         for p in pairs:
             f.write(json.dumps({"messages": [
                 {"role": "user", "content": p["question"]},
                 {"role": "assistant", "content": p["answer"]},
             ]}) + "\n")
+    return pairs
+
+
+def report_variant_counts(pairs: list[dict], label: str) -> None:
+    counts: dict = {}
+    for p in pairs:
+        v = p.get("variant", "legacy")
+        counts[v] = counts.get(v, 0) + 1
+    print(f"{label} per-variant counts: {dict(sorted(counts.items(), key=lambda kv: str(kv[0])))}", flush=True)
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--mode", choices=["full", "topup"], default="full")
+    ap.add_argument("--target-calls", type=int, default=3200)
+    ap.add_argument("--seed", type=int, default=7)
+    ap.add_argument("--topup-variants", type=int, nargs="+", default=[2, 3, 4, 5])
+    ap.add_argument("--topup-per-variant", type=int, default=575)
+    args = ap.parse_args()
+
+    qualifying = load_qualifying()
+    raw_path = Path("data/instructions_raw.jsonl")
+
+    if args.mode == "full":
+        sample = build_full_sample(qualifying, args.target_calls, args.seed)
+    else:
+        sample = build_topup_sample(qualifying, args.topup_variants, args.topup_per_variant, args.seed)
+
+    totals, fail_count, raw_count = run(sample, raw_path)
+
+    pairs = rebuild_instructions(raw_path, Path("data/instructions.jsonl"))
+    report_variant_counts(pairs, "final (post-filter, post-dedupe)")
     # deepseek-v4-flash list price ~$0.08/M in, $0.16/M out
     print(len(pairs), "pairs | tokens", totals, "| est cost $%.2f" % (totals["input"]/1e6*0.08 + totals["output"]/1e6*0.16), flush=True)

src/buddhagpt/datagen.py

@@ -26,6 +26,14 @@ SYSTEM = (
     '{"question": "Example question three?", "answer": "Example answer three, 120-250 words..."}'
 )
 
+def interleaved_variant(call_index: int) -> int:
+    """Map a running call index to a template variant, cycling through all templates.
+
+    Used so any generation run (however many calls it makes) covers every template in
+    TEMPLATES rather than exhausting one variant per full pass over the corpus.
+    """
+    return call_index % len(TEMPLATES)
+
 def build_messages(sutta: dict, variant: int) -> list[dict]:
     tmpl = TEMPLATES[variant % len(TEMPLATES)]
     return [

tests/test_datagen.py

@@ -1,10 +1,17 @@
-from buddhagpt.datagen import build_messages, parse_pairs, dedupe
+from buddhagpt.datagen import build_messages, parse_pairs, dedupe, interleaved_variant
 
 def test_build_messages_varies_templates():
     sutta = {"uid": "mn21", "title": "T", "text": "x" * 900}
     prompts = {build_messages(sutta, v)[1]["content"] for v in range(6)}
     assert len(prompts) == 6  # rotating templates, not one fixed prompt
 
+def test_interleaved_variant_cycles_all_templates():
+    # Any run of >=6 calls must touch every template, not just the first one or two.
+    variants = [interleaved_variant(i) for i in range(18)]
+    assert set(variants) == {0, 1, 2, 3, 4, 5}
+    assert variants[:6] == [0, 1, 2, 3, 4, 5]
+    assert variants == variants[:6] * 3
+
 def test_parse_pairs_extracts_json_lines():
     out = '{"question": "Q1?", "answer": "A1"}\n{"question": "Q2?", "answer": "A2"}'
     assert len(parse_pairs(out, uid="mn21")) == 2