Chunk 54.0

## Current chunk summary This chunk began with a strategic pivot from architecture tuning to data-centric improvements. After reading the DATA_EXPANSION.md plan and original dataset scripts, the user halted the DDTree training run on CT200 to repurpose the 8× RTX PRO 6000 Blackwell GPUs for high-throughput batch inference instead. Setting up SGLang on SM 12.0 (desktop Blackwell) required extensive environment debugging: installing sglang==0.5.12, matching CUDA 13.2 nvcc with pip-installed CUDA headers, creating symlinks for libcudart and libcuda stubs, overlaying CCCL headers from flashinfer's bundled libcudacxx to resolve `nv/target` include errors, and switching to `--attention-backend flashinfer` (FA3/FA4 unsupported on SM120). Once operational, the `extra_buffer` mamba strategy was swapped to `no_buffer`, doubling max concurrent requests from 37 to 72 per GPU and achieving ~1,180 tok/s per GPU (9.4K aggregate). The prompt preparation pipeline was rewritten to extract 193K diverse prompts from Infinity-Instruct-0625 (~99K), WebInstructSub (~40K), CodeFeedback (~29K), MetaMathQA (~24K), Hermes Function Calling v1 (~1.2K with proper tool XML specs), and Agent Training (~553). The generation completed 192,995/193,010 prompts with only 15 failures (0.008%), producing 523M output tokens at ~2,712 avg tokens per completion. These were tokenized and merged with the existing 902K dataset, yielding a combined 1,095,082 samples totaling 2.411B tokens. Training resumed from the step 690 checkpoint with the expanded dataset, but GPU 6 suffered an OOM during ramp-up. The user directed to restart from scratch with all 3 drafter GPUs engaged, preserving the original anchors=1024 and block_size=32 configuration, while tuning non-harmful parameters like batch size to resolve memory pressure.

The Data Expansion Odyssey: From Training Halt to 193K Diverse Prompts on Blackwell GPUs 2515 words

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