Chunk 22.0

In this chunk, the assistant completed the core EAGLE-3 training pipeline implementation and ran it end-to-end on the local machine. After thoroughly exploring the speculators library's training API, the assistant rewrote `04_train.py` to properly use `Eagle3SpeculatorConfig`, `Eagle3DraftModel`, and the built-in `Trainer` class, monkey-patching the verifier weight extraction for Kimi-K2.5's nested config structure. The pipeline was validated on 10 samples (3 epochs in ~1 minute), then scaled to 1000 samples with full hidden state extraction (22.5 min model load + 2.9 min extraction at 2912 tok/s) and training (10 epochs in 27.7 minutes at 6 steps/s). The output checkpoint was verified to be vLLM-compatible with identical weight shapes to the AQ-MedAI reference model and the correct flat config format. The user then redirected the approach toward generating higher-quality training data by capturing Kimi-K2.5's actual reasoning outputs. The assistant wrote `01b_generate_synthetic.py` to feed each question from open-perfectblend independently through the vLLM inference server (at C=128 concurrency, up to 8K completion tokens), capturing both the `reasoning` field and `content` from the model's responses. During testing, two issues emerged: requests timing out due to the default 60s client timeout being too short for long reasoning generations, and the reasoning field not being captured because the script was checking `reasoning_content` instead of the correct `reasoning` attribute. The assistant fixed both issues—increasing the timeout to 1800s and properly extracting reasoning from `msg.reasoning`—and added logic to reconstruct the full token sequence with the correct ` thinking` (token 163606) and ` response` (token 163607) special tokens wrapping the reasoning content. The user capped the run at 10K samples and redirected output to the 3TB `/data` volume.

The Data Quality Pivot: From Pipeline Completion to Synthetic Reasoning Data in EAGLE-3 Training 2991 words

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