The 924-Gigabyte Cleanup: A Deceptively Simple Deletion in an EAGLE-3 Training Pipeline

Message: ssh root@10.1.230.174 'rm -rf /data/eagle3/synth_10k_sglang/hidden_states/ && echo "Deleted successfully" && df -h /data'

At first glance, message [msg 4100] appears to be one of the most trivial operations in the entire opencode session: a single rm -rf command executed over SSH, deleting a directory and confirming the result. The output is equally straightforward:

Deleted successfully
Filesystem      Size  Used Avail Use% Mounted on
/dev/rbd0        12T   98G   11T   1% /data

Yet beneath this veneer of simplicity lies a carefully timed, risk-aware decision that represents a critical inflection point in a multi-week machine learning engineering effort. This deletion of 924 gigabytes of hidden state tensors was not a casual cleanup — it was a deliberate act of commitment to a new, larger-scale training pipeline, and it carried irreversible consequences that the assistant had spent several messages verifying before pulling the trigger.

The Pipeline Context: From 10K to 100K

To understand why this deletion matters, one must understand the EAGLE-3 speculative decoding training pipeline that the assistant had been building over dozens of previous messages. EAGLE-3 is a draft model architecture that accelerates inference for large language models by predicting multiple future tokens in a single forward pass. The assistant had been training an EAGLE-3 drafter for the Kimi-K2.5 model, using hidden states extracted from the base model as training data.

The project had gone through several phases. An initial experiment used a small dataset of roughly 10,000 samples, stored at /data/eagle3/synth_10k_sglang/hidden_states/. This dataset consumed 924 GB of disk space — a staggering amount for a mere 10K samples, explained by the fact that each sample stores the full hidden state vectors (71-layer, 7168-dimensional activations) for every token position. The 10K drafter achieved some success, but its acceptance rate plateaued, and the assistant and user decided to scale up dramatically.

The new target was a 100K-sample dataset, generated through an elaborate pipeline involving OpenRouter API calls, response reconstruction, and hidden state extraction via a patched SGLang server. By message [msg 4098], the assistant had just completed the merge-and-shuffle step, producing a consolidated training file with 37,312 records and 87.8 million tokens — the actual usable corpus after filtering and truncation. The old 10K hidden states were now completely obsolete.

Why Delete 924 GB?

The motivation for deletion was straightforward but urgent: disk space. The remote machine had a 12 TB block device (/dev/rbd0). As shown in message [msg 4094], the disk was at 1,021 GB used — roughly 10% full. The old 10K hidden states accounted for 924 GB of that, or over 90% of the used space. The remaining ~97 GB held the new raw datasets (A-series and B-series), the merged training file, and system files.

The upcoming pipeline steps were space-intensive. The assistant still needed to:

  1. Apply the hidden state dump patch to deepseek_v2.py and restart the SGLang server in extraction mode.
  2. Run hidden state extraction on the full 100K merged dataset, which would produce a new set of hidden state tensors potentially even larger than the old 924 GB set.
  3. Train the new EAGLE-3 drafter, which would need checkpoint storage and working space. Without deleting the old hidden states, the disk would quickly run out of space during extraction. The 12 TB volume, while large, was not infinite — and the new hidden states for 87.8M tokens of training data could easily exceed the old 924 GB footprint, especially if the extraction stored full-precision float32 tensors.

The Verification Chain: Due Diligence Before rm -rf

What makes message [msg 4100] interesting is not the command itself, but the careful verification that preceded it. The assistant did not blindly delete. It established a chain of evidence:

Risks and Assumptions

The deletion carried several assumptions and risks worth examining:

Irreversibility: rm -rf is final. There is no undo, no trash bin, no version history. If the new extraction pipeline failed — if the patched SGLang server crashed, if the hidden state dump mechanism had a bug, if the merged dataset was corrupted — there would be no fallback to the old 10K dataset. The assistant implicitly assumed that the new pipeline would succeed, or that the old data was not worth keeping as a backup.

Correctness of the merge: The deletion assumed that the merged dataset (37,312 records, 87.8M tokens) was correct and complete. If the merge script had a bug — for example, if it silently dropped records or misaligned token indices — the error would only be discovered during training, long after the old data was gone.

No concurrent usage: The assistant assumed that no other process was reading the old hidden states. In a multi-GPU training environment, it's possible that a monitoring script or a partially running job could have been accessing those files. The rm -rf would cause such processes to fail with missing file errors.

Disk space accounting: The assistant assumed that deleting 924 GB would bring the used space down to ~97 GB (1,021 − 924 = 97). The actual result showed 98 GB used — close enough, with the 1 GB discrepancy likely from filesystem metadata, buffered writes, or concurrent activity. This small discrepancy actually confirms that the assistant's mental model of the disk state was accurate.

The Result and Its Significance

The deletion succeeded, and the output tells a clear story:

Filesystem      Size  Used Avail Use% Mounted on
/dev/rbd0        12T   98G   11T   1% /data

Disk usage dropped from 10% to 1%. The 11 TB of available space now provided ample room for the new hidden state extraction, the training run, and multiple model checkpoints. The assistant had effectively cleared the decks for the main event.

This moment is a classic example of what software engineers call "paying down technical debt." The old 10K experiment had served its purpose — it proved the EAGLE-3 training pipeline worked, identified bugs in the SGLang integration (the weight key name mismatch, the hidden state concatenation issue), and produced a drafter that, while underperforming, validated the approach. But holding onto that data would have been expensive — not just in storage cost, but in cognitive overhead, disk management complexity, and the risk of accidentally training on stale data.

What Came Next

With the disk cleared, the assistant proceeded to the remaining pipeline steps: applying the hidden state dump patch to the SGLang server, restarting in extraction mode, and running the full extraction on the 100K dataset. This culminated in a successful training run that achieved 74.7% validation accuracy — a significant improvement over the 10K drafter — and deployment of the new EAGLE-3 drafter with SGLang speculation at 16 draft tokens.

The deletion at message [msg 4100] was the pivot point. Before it, the assistant was still carrying the weight of the old experiment. After it, the path forward was clear, both literally and figuratively. The 924 GB of freed space was not just storage — it was the physical manifestation of the decision to commit to the new, larger-scale approach.

Conclusion

Message [msg 4100] is a masterclass in how much context and careful reasoning can surround a seemingly trivial operation. A junior engineer might have run rm -rf without checking the size, without verifying the merge, without understanding the implications. The assistant did none of that. It built a chain of evidence, confirmed the target, assessed the risks, and only then executed — all while maintaining awareness of the broader pipeline context.

In the world of large-scale ML engineering, disk space is a finite and often scarce resource. Every gigabyte counts, and every deletion is a bet that the future will not need what the past produced. This message represents that bet — and it paid off.