Chunk 23.0

In this chunk, the assistant completed the full EAGLE-3 training pipeline and then hit a significant roadblock with vLLM integration. First, the synthetic data generation script was fixed to properly wrap reasoning with ` thinking`/` response` tokens and use the correct `msg.reasoning` attribute. The 10K inference run completed successfully in ~5.3 hours with zero errors and 100% reasoning capture. Hidden state extraction ran at 3,165 tok/s, producing 828 GB of training data, followed by a 5-epoch finetune from the AQ-MedAI checkpoint that completed in 2.6 hours. When testing the trained drafter with vLLM EAGLE-3, three patches were required to make it work with DeepSeek V3/Kimi-K2.5 (model whitelist, image token handling, and SupportsEagle3 interface implementation). Despite successful loading, both the newly trained drafter and the pre-trained AQ-MedAI baseline achieved only ~15% acceptance rate, resulting in 0.66x throughput—worse than no speculation. This confirmed a fundamental vLLM integration issue with MLA attention hidden state extraction during decode, not a training quality problem. The user directed the assistant to pivot to SGLang, which has first-class EAGLE-3 support and is explicitly tested with Kimi-K2 drafters. The assistant built sgl-kernel for SM120 (48 minutes) and verified it works. Base SGLang loads the 547GB model in just 22 seconds (vs 25 minutes in vLLM), but the server processes appear to be deadlocking after weight loading—zero CPU/GPU utilization with no listening port. Debugging with verbose logging and CUDA graph disabling is ongoing to resolve this SM120 compatibility issue.

The EAGLE-3 Odyssey: From Training Triumph to Integration Crisis on a 1-Trillion-Parameter Model 1912 words

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