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[gemma_cpp] Add support for Qwen3 models (0.6B, 1.7B and 4B). - #964

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[gemma_cpp] Add support for Qwen3 models (0.6B, 1.7B and 4B).#964
copybara-service[bot] merged 1 commit into
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@copybara-service copybara-service Bot commented Jul 23, 2026

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[gemma_cpp] Add support for Qwen3 models (0.6B, 1.7B and 4B).

Qwen3 shares a very similar architecture to Gemma3 (comparison).
Differences include:

  • Tokenizer : Qwen3 uses bytelevel BPE while Gemma3 uses Sentencepiece;
  • Embedding : Qwen3 0.6B and 1.7B do not share the input_embedding and lm_head weights, and there is no Embedding Scaling for Qwen3;
  • Activation : Qwen3 uses SiLU while gemma3 uses GeLU;
  • Norm : Qwen3 only has two pre-norms, and they are not Zero-centered (we substract 1.0 while converting the weights to keep the kernel untouched);
  • Attention : Qwen3 uses full-attention for all the layers.
  • Prompt Wrapping : Qwen3 uses different special tokens and does not require .

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copybara-service Bot force-pushed the test_952434363 branch 3 times, most recently from 7474b01 to 93d09d3 Compare July 27, 2026 23:55
Qwen3 shares a very similar architecture to Gemma3 ([comparison](https://sebastianraschka.com/llm-architecture-gallery/?compare=qwen3-0-6b%2Cgemma-3-270m#architecture-diff-tool)).
Differences include:
- *Tokenizer* : Qwen3 uses bytelevel BPE while Gemma3 uses `Sentencepiece`;
- *Embedding* : Qwen3 0.6B and 1.7B do not share the `input_embedding` and `lm_head` weights, and there is no Embedding Scaling for Qwen3;
- *Activation* : Qwen3 uses SiLU while gemma3 uses GeLU;
- *Norm* : Qwen3 only has two pre-norms, and they are not Zero-centered (we substract 1.0 while converting the weights to keep the kernel untouched);
- *Attention* : Qwen3 uses full-attention for all the layers.
- *Prompt Wrapping* : Qwen3 uses different special tokens and does not require <BOS>.

PiperOrigin-RevId: 954918486
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copybara-service Bot merged commit 891e43d into dev Jul 28, 2026
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copybara-service Bot deleted the test_952434363 branch July 28, 2026 00:22
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