Complete FTPO training run; document orchestration bug fix and thermal shutdown

Ran the full FTPO fine-tune to completion: 4h12m wall-clock, early-stopped at
step 380/750 by the configured chosen_win=0.85 threshold, train_loss 4.0->2.19.
Output is a merged 16-bit model plus LoRA adapter.

Also fixes an UnboundLocalError in core/orchestration.py when resuming a run
with generation disabled (--generation-step-enabled false), which is needed to
re-run just the finetune stage against an already-generated dataset.

Documents an unrelated platform incident: an uncapped multi-hour finetune
triggered a thermal shutdown mid-run with no checkpoint saved, requiring a
restart from step 0. Capping GPU clocks/power before the retry let it complete
without issue.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0141RdLKiMeXLMXsWyb4U3B5

DGX_SPARK_SETUP.md

@@ -131,8 +131,8 @@ serving 50 concurrent generation threads.
 ## 6. Code patches needed for current library versions
 
 The repo (as of the commit we tested against) predates some of the exact library versions that
-`pip install` resolves to today. Three small patches were needed — none are DGX-Spark-specific,
+`pip install` resolves to today. Four small patches were needed — none are DGX-Spark-specific,
-they'd bite on any platform once these versions are current on PyPI:
+they'd bite on any platform in the same circumstances:
 
 **`utils/vllm_manager.py`** — vLLM 0.26.0 removed the `--disable-log-requests` flag (replaced by an
 opt-in `--enable-log-requests`, off by default already). Delete the line:
@@ -155,6 +155,23 @@ training, so all-zero/all-text is correct):
 - the reference-model forward pass, `self.ref_model is None` branch (inside `null_ref_context()`)
 - the reference-model forward pass, `self.ref_model is not None` branch
 
+**`core/orchestration.py`** — resuming a run with generation disabled
+(`--generation-step-enabled false`, used to re-run just the finetune stage against an
+already-generated dataset) crashes:
+```
+UnboundLocalError: cannot access local variable 'banned_ngrams_json_path' where it is not associated with a value
+```
+`banned_ngrams_json_path` / `banned_slop_phrases_json_path` are only ever assigned inside the
+`if generation_enabled:` block, but a later resume-logging block references them unconditionally.
+Guard that block with the same flag:
+```python
+if start_iter_idx > 0 and generation_enabled:
+    if banned_ngrams_json_path.exists(): ...
+```
+(There's a second, non-fatal instance of the same root cause — `iter0_output_file_for_dpo`
+referenced before assignment when loading stats from a prior completed run — it's caught and
+logged as a warning rather than raised, so it doesn't block anything, but it's the same bug shape.)
+
 ## 7. FTPO fine-tuning was slow because of fixed-length padding, not raw compute
 
 With `finetune_batch_size: 1` / `gradient_accumulation_steps: 16`, we measured ~750 optimizer steps
@@ -210,18 +227,40 @@ run.
 If you need it faster still, the other lever is `finetune_max_train_examples` (fewer total steps,
 less data coverage) — or just accept the ~8h runtime and let it run in the background.
 
+## 8. Sustained full-GPU load can trigger a thermal shutdown
+
+An unthrottled attempt at the full finetuning run caused the machine to shut down from heat partway
+through (no OOM/crash signature in the logs — the process and GPU state simply vanished when the
+box power-cycled). No checkpoint had been written yet, so the run restarted from step 0 — FTPO
+doesn't checkpoint mid-run by default, so a mid-run shutdown here means losing all progress so far.
+
+Fix: cap GPU clocks/power before starting a sustained multi-hour job (finetuning, not the bursty
+generation step):
+```bash
+nvidia-smi -pl <lower_watts>          # if supported on this platform
+# or check nvidia-smi -q -d CLOCK,POWER for current caps and headroom
+```
+With clocks capped, the retry ran the full ~4h12m to completion without incident — GPU temperature
+held in the 71-81°C range under sustained 90%+ utilization throughout.
+
 ## Validated results
 
 Ran the full pipeline against `unsloth/gemma-3-4b-it` (2 iterations, 1200 prompts each):
 
 - Iteration 0 (baseline, no bans): completed in 28m24s, `repetition_per_100k_chars` = 160
 - Iteration 1 (with ban lists from iteration 0's analysis): completed in 1h31m43s (slower — active
-  backtracking around bans), `repetition_per_100k_chars` = 56 — a real, measured reduction in slop
+  backtracking around bans), `repetition_per_100k_chars` = 56 — a real, measured reduction in slop.
-- FTPO training: confirmed working end-to-end (750 steps, 12,000 preference pairs) after the
+  Both numbers are from a run predating the `refusal_detector.py` fix in §6, so they reflect
-  patches in §6. At the original `finetune_max_seq_length: 4000`, steady-state was ~160-185s/step
+  n-gram/phrase-ban slop reduction only, not refusal-aware filtering.
-  (~34h for the full run). After the fix in §7 (`finetune_max_seq_length: 1280`), measured
+- FTPO training: **ran to completion.** With the fixes from §6 and §7
-  ~40s/step over a 30-minute validation run — a confirmed ~4.3x speedup, ~8.3h extrapolated for the
+  (`finetune_max_seq_length: 1280`), trained on the 12,000-example preference-pair dataset (quota-
-  full 750 steps. Not run to full completion.
+  sampled from 58,369 raw pairs) at `finetune_batch_size: 1` / `gradient_accumulation_steps: 16`.
+  Stopped early by the configured `finetune_early_stopping_wins: 0.85` threshold — `chosen_win`
+  (fraction of chosen completions preferred over rejected) crossed 0.85 at step 380 of a possible
+  750 (1 epoch). `train_loss` fell from ~4.0 to 2.19 over the run. Total wall-clock time
+  (training + LoRA save + 16-bit merge): **4h12m32s**, steady-state ~40s/step throughout — matching
+  the §7 prediction. Output: LoRA adapter and a merged 16-bit model (8.1GB) under
+  `finetuned_model_ftpo_exp01/`.
 
 ## Quick-reference: full env setup
 

core/orchestration.py

@@ -387,7 +387,7 @@ def orchestrate_pipeline(config: Dict[str, Any], experiment_dir: Path, resume_mo
             start_iter_idx = max_found_iter + 1
             logger.info(f"Resuming from iteration {start_iter_idx}.")
             # Log presence of existing ban lists if resuming past iter 0
-            if start_iter_idx > 0:
+            if start_iter_idx > 0 and generation_enabled:
                 if banned_ngrams_json_path.exists(): logger.info(f"Resuming with existing n-gram ban list: {banned_ngrams_json_path}")
                 else: logger.info("No existing n-gram ban list found to resume with for subsequent iterations.")
                 if banned_slop_phrases_json_path.exists(): logger.info(f"Resuming with existing slop phrase ban list: {banned_slop_phrases_json_path}")