The Hidden State Estimation: A Critical Planning Pivot in the EAGLE-3 Training Pipeline

Introduction

In the sprawling, multi-phase pipeline of training a speculative decoding drafter for a large language model, there comes a moment when the data generation phase ends and the compute-intensive extraction phase must begin. Between these two phases lies a crucial planning question: how much disk space will the hidden state extraction require? The answer determines whether the pipeline can proceed on existing infrastructure, whether storage must be expanded, or whether the dataset itself must be pruned. Message [msg 4086] in this opencode session captures that exact moment of reckoning — a single assistant message that takes stock of the complete dataset, estimates the storage footprint of the coming extraction, and surfaces a critical insight about the A1_deepswekimi dataset's disproportionate impact on the pipeline.

This message, though outwardly a straightforward technical calculation, represents a pivotal decision point in the entire EAGLE-3 training effort. It is the bridge between data generation and model training, the moment when abstract token counts become concrete storage requirements, and the point at which the team must decide whether to proceed, expand, or cut.

Context and Motivation: Why This Message Was Written

To understand why this message exists, one must understand the arc of the broader session. The team had been working for days — across 29 segments and thousands of messages — to train an EAGLE-3 speculative decoding drafter for the Kimi-K2.5 model. The pipeline had progressed through multiple phases: setting up the environment with NVIDIA drivers and CUDA ([segment 0]), deploying SGLang for inference, debugging the EAGLE-3 hidden state concatenation bug ([segment 27]), scaling up training data by 10× ([segment 27]), and finally pivoting to OpenRouter API for generating the B-datasets ([segment 29]).

By the time of message [msg 4086], the OpenRouter inference had completed spectacularly — all eight B-datasets (B3 through B8) finished in just 33 minutes at a cost of approximately $86. The assistant had just presented the final tally in [msg 4080]: 35,314 B-dataset samples totaling 90.9M tokens, plus the pre-tokenized A-datasets (A1_deepswekimi at 44.9M tokens and A2_kimik25 at 2.6M tokens), for a grand total of approximately 40,114 samples and 138.4M tokens.

The user's response in [msg 4082] was direct: "Continue next stages, merge (prompt/think/resp/tool), shuffle, tokenize and perform next steps; Assess how much space is needed for hidden state extraction - might need VM data disk to be expanded. Possibly remove old 10k hidden state extraction to free 1TB."

This instruction contained two key directives. First, proceed with the merge-and-shuffle phase. Second — and more critically — assess the disk space requirements for hidden state extraction, with the explicit possibility that the VM's data disk might need expansion and that the old 10K hidden states (weighing in at 924GB) could be deleted to reclaim space. The assistant's response in [msg 4083] acknowledged this with a todo list, and [msg 4084] gathered the current disk state: 11TB available on /data, 920GB free on /shared, and the old 10K hidden states consuming 924GB.

Message [msg 4086] is the direct response to the space assessment directive. It is the moment of truth — the calculation that determines whether the pipeline can proceed on existing hardware.

The Estimation Methodology: How the Calculation Was Performed

The assistant's approach to the space estimation is methodical and precise. Rather than making rough guesses based on sample counts, it writes a Python script that reads the actual tokenized data files and computes exact storage requirements. The script, embedded directly in the message via a heredoc, performs several distinct calculations:

Per-dataset token counting. The script iterates over all ten datasets (A1, A2, B1 through B8), loading each tokenized_data.jsonl file and summing the seq_len field for every record. This gives an exact token count per dataset rather than an estimate. The results are printed in a formatted table showing samples, total tokens in millions, and average tokens per sample.

Per-token storage calculation. The script then computes the storage required per token based on the known extraction format. The EAGLE-3 training pipeline requires hidden states from three specific layers of the model — layers 2, 30, and 58 — at every token position. Each hidden state is a float16 vector of size 7168 (the model's hidden_size). The calculation is straightforward:

bytes_per_token = 3 layers × 7168 hidden_dim × 2 bytes (fp16) = 43,008 bytes ≈ 42 KB

Total space estimation. Multiplying the per-token cost by the total token count yields the raw estimate. The script then adds 5% overhead for numpy file headers, metadata, and filesystem overhead, producing a final estimate.

Comparison with the old extraction. The old 10K sample extraction consumed 924GB. By computing the implied token count of the old extraction (924GB divided by the per-token cost), the script establishes a baseline for comparison. It then computes the ratio of samples (4×) and the ratio of estimated space (~6×), revealing that the new dataset is disproportionately larger per-sample due to the ultra-long A1_deepswekimi samples.

The script is shipped to the remote server via scp and executed there, ensuring it runs against the actual data files in their production location. The output is captured and displayed inline.

Assumptions and Their Validity

Every estimation rests on assumptions, and this one is no exception. The assistant makes several implicit and explicit assumptions that deserve scrutiny:

Hidden state format. The script assumes hidden states are stored as float16 (2 bytes per value). This is the standard precision for transformer hidden states in training pipelines — float32 would double the storage requirement to 86 KB per token, while float8 would halve it. The assumption is well-justified: the old 10K extraction used the same format, and the EAGLE-3 training pipeline operates on float16 activations.

Three specific layers. The script assumes extraction from layers 2, 30, and 58. This is a design choice for the EAGLE-3 drafter — using early, middle, and late layers to capture hierarchical representations. The assumption is validated by the existing pipeline code and the old extraction's format.

Hidden size of 7168. This is the model architecture's defined hidden dimension for Kimi-K2.5. It is a known constant, not an estimate.

5% overhead. The script adds 5% for numpy file headers and filesystem overhead. This is a reasonable rule-of-thumb for large numpy arrays stored as .npy files, though actual overhead can vary depending on the number of files (one per sample or one per layer per sample) and filesystem block sizes.

Comparability of old and new extractions. The script implicitly assumes the old 10K extraction used the same format (same layers, same precision, same hidden size). This is a safe assumption given that both extractions serve the same EAGLE-3 training pipeline.

None of these assumptions are obviously wrong. The estimation methodology is sound and the assumptions are well-grounded in the known architecture and pipeline design.

Input Knowledge Required to Understand This Message

To fully grasp the significance of message [msg 4086], one must understand several pieces of prior knowledge:

The Kimi-K2.5 model architecture. The model has a hidden dimension of 7168, which determines the per-token storage cost. Without knowing this, the byte calculation would be meaningless.

The EAGLE-3 training pipeline. EAGLE-3 is a speculative decoding framework that trains a lightweight "drafter" model to predict the base model's hidden states. The training requires hidden states from multiple layers of the base model at every token position. The choice of layers 2, 30, and 58 is specific to this implementation.

The dataset structure. The pipeline uses ten datasets organized into A (pre-tokenized, from earlier runs) and B (newly generated via OpenRouter and local SGLang). Each dataset has a tokenized_data.jsonl file containing records with a seq_len field.

The old 10K extraction. A previous hidden state extraction of approximately 10,000 samples consumed 924GB of disk space. This serves as the baseline for comparison and is the candidate for deletion to free space.

The disk layout. The system has /data mounted on a 12TB RBD device (11TB free), /shared on a 1.7TB NFS volume (920GB free), and root on an 800GB local disk. The hidden states will be stored on /data.

The OpenRouter pivot. The B-datasets were generated via OpenRouter API rather than local GPU inference, which dramatically accelerated the data generation phase. The assistant had just confirmed that all B-datasets completed successfully.

Output Knowledge Created by This Message

Message [msg 4086] produces several critical pieces of knowledge that directly inform the next steps of the pipeline:

Exact token counts per dataset. The script reveals the precise token distribution across all ten datasets. The standout finding is A1_deepswekimi: 2,800 samples consuming 44.9M tokens, with an average sequence length of 16,025 tokens per sample. This is an order of magnitude longer than any other dataset — B2_opencodeinstruct averages 3,899 tokens, and most others average between 1,200 and 3,100 tokens. A single dataset with 7% of the samples accounts for 32% of the total tokens.

Total token count. The grand total is 138.4M tokens across 40,114 samples. This is the denominator for all subsequent storage and time estimates.

Estimated hidden state extraction size. The raw estimate is approximately 5.5 TB, or 5.8 TB with 5% overhead. This is a staggering number — more than six times the size of the old 10K extraction (924GB), despite having only four times as many samples. The discrepancy is explained by the A1_deepswekimi dataset's extreme average sequence length.

Available disk space after deletion. Deleting the old 10K hidden states would free 924GB, bringing available space on /data from 11TB to approximately 11.9TB. This is more than enough to accommodate the estimated 5.5-5.8TB extraction, with room to spare.

The A1_deepswekimi problem. The estimation surfaces a critical insight: A1_deepswekimi's 2,800 samples, with an average length of 16,025 tokens, dominate the token budget. These samples alone would require approximately 1.9TB of hidden state storage (44.9M tokens × 43,008 bytes/token). The extraction time would also be dominated by these samples — at typical throughput rates, processing 44.9M tokens through the model for hidden state extraction could take 30-40 hours just for this dataset.

The A1_deepswekimi Insight: A Hidden Bottleneck

The most valuable output of message [msg 4086] is not the total estimate but the per-dataset breakdown that reveals A1_deepswekimi as a bottleneck. This dataset contains "deep swe" (deep swe) agent trajectories — long, multi-turn conversations where an AI agent interacts with tools and environments. These trajectories are structurally important for training the drafter to handle agentic workflows, but their extreme length creates disproportionate costs in both storage and computation.

The chunk summary from the analyzer (see [chunk 29.0]) explicitly notes this: "A1_deepswekimi's 2800 ultra-long samples (44.9M tokens, avg 16K/sample) dominate the token budget — capping sequence length at 8192 and potentially dropping A1 would reduce extraction from ~5.5TB/91h to ~3.5TB/72h." This suggests that the team was already considering mitigation strategies: either truncating sequences to 8192 tokens (halving the effective token count for A1) or dropping the dataset entirely. Both options would significantly reduce storage and compute requirements.

The estimation thus serves not just as a space calculation but as a dataset quality assessment tool. It reveals which parts of the dataset are driving costs and enables informed decisions about data curation.

The Thinking Process: Methodical and Data-Driven

The thinking process visible in message [msg 4086] is characteristic of the assistant's approach throughout the session: methodical, data-driven, and precise. The assistant does not guess or approximate. It writes code, ships it to the production environment, runs it against real data, and presents the results clearly.

The structure of the message reveals the reasoning flow:

1. State the current situation. "Good. 40,114 samples total, 924GB old 10K hidden states, 11TB disk with 11TB free." This establishes the baseline.
2. Write the estimation script. The script is comprehensive, covering all datasets, computing exact token counts, calculating per-token storage, and comparing with the old extraction.
3. Execute and capture results. The script runs on the remote server where the data lives, ensuring accuracy.
4. Present findings. The output shows per-dataset breakdowns, totals, and the space estimate. The script itself demonstrates careful thinking. It handles the case where a dataset file might not exist (graceful FileNotFoundError handling). It computes both raw estimates and estimates with overhead. It compares the new extraction with the old one, computing ratios that immediately reveal the A1_deepswekimi anomaly. The output formatting is clean and readable, with aligned columns and clear section headers.

Implications for the Pipeline

The knowledge created by message [msg 4086] directly informs the next steps of the pipeline. The merge-and-shuffle phase can proceed with confidence, knowing that the final dataset is 40,114 samples and 138.4M tokens. The hidden state extraction phase can be planned with accurate storage estimates: approximately 5.5-5.8 TB, well within the ~11.9TB available after deleting the old extraction.

But the message also raises strategic questions. Should A1_deepswekimi be truncated to 8192 tokens? Should it be dropped entirely? The 91-hour extraction time estimate (from the chunk summary) versus 72 hours with truncation represents a meaningful tradeoff between dataset completeness and pipeline velocity. These are decisions that the team must make, armed with the data from this estimation.

The message also validates the decision to use OpenRouter for data generation. The B-datasets, generated at high speed and low cost, contribute 90.9M tokens at an average cost of less than $1 per million tokens. Without OpenRouter, generating this data on local GPUs would have taken days instead of 33 minutes.

Conclusion

Message [msg 4086] is a deceptively simple technical message that carries enormous strategic weight. It is the moment in the pipeline where abstract planning meets concrete resource constraints, where token counts become terabytes, and where the team must confront the practical realities of their ambitious data scaling effort. The estimation is thorough, the assumptions are sound, and the output knowledge is actionable.

The message also exemplifies a pattern that recurs throughout the session: the assistant's preference for precise, code-driven estimation over hand-waving approximation. Rather than saying "roughly 5-6 TB," the assistant writes a script, runs it against real data, and presents exact numbers. This methodological rigor is what allows the team to make informed decisions about disk expansion, dataset pruning, and pipeline prioritization.

In the end, the estimation reveals that the pipeline is feasible on existing hardware — barely. The 5.5 TB estimate, while large, fits within the available space after cleanup. But the A1_deepswekimi insight lingers as a challenge: the longest samples in the dataset are consuming resources disproportionate to their count, and decisions about their treatment will shape the efficiency of the entire extraction phase. Message [msg 4086] provides the data needed to make those decisions wisely.