Segment 29

This sub-session pivoted from local GPU inference to OpenRouter API for generating EAGLE-3 training data, building a new `run_inference_openrouter.py` script with 2000-concurrent request handling, provider routing, and resume support. The critical technical challenge of reconstructing exact Kimi-K2.5 token IDs from OpenRouter's text responses was solved through careful analysis of special token encoding (discovering `<|im_end|>` is token 163586, not 163533), verifying BPE boundary behavior across ` response` separators, and confirming tool call tokens survive as raw text. All 8 B-datasets (B3-B8) were completed in ~33 minutes at ~$86 cost, with structural validation of 1637 responses showing 0 issues and token counts matching billing within 0.04%. The remaining phases were scoped: merging ~40K samples (138.4M tokens) into a shuffled dataset, then hidden state extraction, with analysis of A1_deepswekimi's long samples leading to consideration of sequence length capping or dropping A1 to reduce extraction time from ~91h to ~72h. A merge-and-shuffle script was written, and old 924GB 10K hidden states were identified for deletion.

The $86, 33-Minute Pivot: How OpenRouter Rescued EAGLE-3 Training Data Generation 4052 words

Chunks

• The $86 Pivot: Reconstructing Exact Token IDs at 2000 Concurrency for EAGLE-3 Training
  3505 words
• The $86 Pivot: How OpenRouter Rescued EAGLE-3 Training Data Generation
  2209 words