Update HF Jobs training script

scripts/hf_jobs_train_syllabus.py

@@ -0,0 +1,124 @@
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#   "huggingface_hub>=0.26.0",
+# ]
+# ///
+"""Run syllabus LoRA SFT on Hugging Face Jobs (GPU).
+
+Downloads finetune JSONL from the Hub, clones training-pipeline, runs readiness
+gates, trains with OOM-safe defaults, and pushes the adapter to a Hub model repo.
+
+Override via environment variables:
+  HF_DATASET_REPO   (default: Dev-the-dev91/syllabus-finetune)
+  HF_OUTPUT_MODEL   (default: Dev-the-dev91/syllabus-extractor-lora)
+  HF_PIPELINE_REPO  (default: https://github.com/madch3m/training-pipeline.git)
+  HF_MAX_LENGTH     (default: 2048)
+  HF_TRAIN_EPOCHS   (default: 3)
+  HF_JOB_FLAVOR     (informational only when run locally)
+"""
+
+from __future__ import annotations
+
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+from huggingface_hub import HfApi, hf_hub_download
+
+HF_USER = os.environ.get("HF_USER", "Dev-the-dev91")
+DATASET_REPO = os.environ.get("HF_DATASET_REPO", f"{HF_USER}/syllabus-finetune")
+OUTPUT_MODEL = os.environ.get("HF_OUTPUT_MODEL", f"{HF_USER}/syllabus-extractor-lora")
+PIPELINE_GIT = os.environ.get(
+    "HF_PIPELINE_REPO",
+    "https://github.com/madch3m/training-pipeline.git",
+)
+MAX_LENGTH = os.environ.get("HF_MAX_LENGTH", "2048")
+NUM_EPOCHS = os.environ.get("HF_TRAIN_EPOCHS", "3")
+WORK = Path(os.environ.get("HF_WORK_DIR", "/tmp/training_pipeline"))
+
+
+def run(cmd: list[str], *, cwd: Path | None = None) -> None:
+    print("+", " ".join(cmd), flush=True)
+    subprocess.run(cmd, check=True, cwd=cwd)
+
+
+def main() -> None:
+    api = HfApi()
+    who = api.whoami()["name"]
+    print(f"Hub user: {who}")
+    print(f"Dataset: {DATASET_REPO}")
+    print(f"Output model: {OUTPUT_MODEL}")
+
+    if WORK.exists():
+        run(["rm", "-rf", str(WORK)])
+    run(["git", "clone", "--depth", "1", PIPELINE_GIT, str(WORK)])
+    run([sys.executable, "-m", "pip", "install", "-q", "-e", ".[train]"], cwd=WORK)
+
+    data_dir = WORK / "data" / "finetune"
+    data_dir.mkdir(parents=True, exist_ok=True)
+    for name in ("train.jsonl", "valid.jsonl"):
+        cached = hf_hub_download(
+            repo_id=DATASET_REPO,
+            filename=name,
+            repo_type="dataset",
+        )
+        (data_dir / name).write_bytes(Path(cached).read_bytes())
+        print(f"Fetched {name} from {DATASET_REPO}")
+
+    train_path = data_dir / "train.jsonl"
+    valid_path = data_dir / "valid.jsonl"
+
+    run(
+        [
+            sys.executable,
+            "validate_training_readiness.py",
+            "--train-jsonl",
+            str(train_path),
+            "--valid-jsonl",
+            str(valid_path),
+            "--strict",
+        ],
+        cwd=WORK,
+    )
+
+    out_dir = WORK / "artifacts" / "hf_syllabus_extractor"
+    run(
+        [
+            sys.executable,
+            "train_hf_structured_extractor.py",
+            "--train-jsonl",
+            str(train_path),
+            "--valid-jsonl",
+            str(valid_path),
+            "--model-name",
+            "Qwen/Qwen2.5-0.5B-Instruct",
+            "--output-dir",
+            str(out_dir),
+            "--max-length",
+            MAX_LENGTH,
+            "--per-device-train-batch-size",
+            "1",
+            "--gradient-accumulation-steps",
+            "8",
+            "--num-train-epochs",
+            NUM_EPOCHS,
+            "--bf16",
+            "--disable-mlflow",
+        ],
+        cwd=WORK,
+    )
+
+    api.create_repo(OUTPUT_MODEL, repo_type="model", exist_ok=True)
+    api.upload_folder(
+        folder_path=str(out_dir),
+        repo_id=OUTPUT_MODEL,
+        repo_type="model",
+        commit_message="LoRA adapter from HF Jobs syllabus SFT",
+    )
+    print(f"Uploaded adapter: https://huggingface.co/{OUTPUT_MODEL}")
+
+
+if __name__ == "__main__":
+    main()