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- Google Gemma 2 9B with GQA (16 heads, 8 KV heads), head_dim=256 - 42 decoder layers, RMSNorm, GELU tanh, tied embeddings - Final logit softcapping (30.0) - Custom Gemma2NeuronConfig for model-specific settings - Validated: 99.38% teacher-forced / 84.69% greedy (TP=8, seq_len=128, bf16) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
47.1 tok/s, CE MBU=1.3%/MFU=0.6%, TG MBU=2.3%/MFU=0.0% Profiled on trn1.32xlarge, TP=8, seq_len=128, BF16. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
First encoder-decoder (seq2seq) model in contrib, following the Whisper pattern with separate encoder/decoder NeuronApplicationBase subclasses. Key fixes applied to the original handoff port: - Added encoder attention mask to prevent attention to padding tokens - Added cross-attention mask to prevent decoder attending to encoder padding - Fixed cross-attention to always compute K/V from encoder output (prevents Neuron tracer dead code elimination of encoder output during decode) - Fixed decoder dispatch between prefill and decode model wrappers Validated: 87.9% token match across 5 prompts (TP=8, float32). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Summary
Test plan
pytest contrib/models/blenderbot-3B/test/integration/test_blenderbot_inference.py🤖 Generated with Claude Code