Segment 22
In this sub-session, the assistant finalized the EAGLE-3 training pipeline by rewriting `04_train.py` to properly use the speculators library's `Eagle3SpeculatorConfig`, `Eagle3DraftModel`, and built-in `Trainer` class, including monkey-patching for Kimi-K2.5's nested config. The pipeline was validated on 10 samples (~1 minute for 3 epochs) and then scaled to 1000 samples, with hidden state extraction running at 2912 tok/s and training completing 10 epochs in 27.7 minutes. The output checkpoint was verified to be vLLM-compatible with correct weight shapes and flat config format. Following the user's direction to improve data quality, the assistant wrote `01b_generate_synthetic.py` to feed each question from open-perfectblend through the vLLM inference server at high concurrency, capturing both the `reasoning` field and `content` from the model's responses. Two issues were fixed during testing: the default 60s client timeout was increased to 1800s to handle long reasoning generations, and the reasoning extraction was corrected to use `msg.reasoning` instead of `reasoning_content`. The script was configured to reconstruct the full token sequence with special thinking/response tokens, and the user capped the run at 10K samples, redirecting output to the `/data` volume.