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Looks good overall — the config threshold bumps, kwarg cleanup, and build fix are all straightforward. One concern flagged inline: _load_config_skip_layer_type_validation mutates a shared class-level list without synchronization, which is a thread-safety risk if config loading ever happens concurrently.
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…nchmark configs - Bump hf_kl_threshold for qwen3_moe_30b_hellaswag (1e-4 -> 1e-3) and gpt_oss_20b (5e-2 -> 1e-1) to accommodate observed KL divergence in checkpoint robustness tests. - Reduce lr for qwen3_moe_30b_hellaswag (1e-3 -> 1e-4). - Remove position_ids, qkv_format, cu_seqlens, and seq_index kwargs from the Qwen3NextGatedDeltaNet call in Block.forward() — the upstream HF implementation does not accept these arguments. - Add trust_remote_code to AutoConfig.from_pretrained in Step-3.5-Flash benchmark configs (step_3.5_flash_te_deepep, step35flash_lora). - Replace placeholder /path/to/model with actual model name in nemotron_super_v3_te_deepep benchmark config. Signed-off-by: hemildesai <hemild@nvidia.com> Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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akoumpa
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Apr 17, 2026
This was referenced Apr 21, 2026
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Summary
hf_kl_thresholdforqwen3_moe_30b_hellaswag(1e-4 → 1e-3) andgpt_oss_20b(5e-2 → 1e-1) to fix checkpoint robustness test failures where observed KL divergence slightly exceeded the thresholdposition_ids,qkv_format,cu_seqlens, andseq_indexkwargs from theQwen3NextGatedDeltaNetcall — the upstream HFforward()does not accept these (linear attention uses conv1d + recurrent state, not rotary embeddings)Test plan
qwen3_moe_30b_hellaswaggpt_oss_20bqwen3_next_te_deepep.yaml) runs withoutTypeError🤖 Generated with Claude Code