feat: QLoRA fine-tune v1 on M5 + fused model (M2)

scripts/prepare_train.py

@@ -0,0 +1,12 @@
+import json, random
+from pathlib import Path
+
+rows = [json.loads(l) for l in Path("data/instructions.jsonl").read_text().splitlines()]
+random.seed(7); random.shuffle(rows)
+n_val = max(200, len(rows) // 20)
+Path("data/train").mkdir(parents=True, exist_ok=True)
+for name, part in [("valid", rows[:n_val]), ("train", rows[n_val:])]:
+    with Path(f"data/train/{name}.jsonl").open("w") as f:
+        for r in part:
+            f.write(json.dumps(r) + "\n")
+print(len(rows) - n_val, "train /", n_val, "valid")