Gemma4 PyTorch Claude — AI skill for Claude Code
Standalone Gemma 4 PyTorch Model using Claude Code.
How to install Gemma4 PyTorch Claude
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open rwightman/gemma4_pytorch_claude and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Gemma4 PyTorch Claude does
Standalone Gemma 4 PyTorch Model using Claude Code.
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README
gemma4-pt-claude
A clean, standalone PyTorch implementation of Google's **Gemma 4** model family — ported layer-by-layer from the official JAX reference with numerical verification at every step.
Text, vision, and audio. No mystery abstractions. Just PyTorch and matrix multiplies.
Supported Models
| Variant | Layers | Embed | Heads | KV Heads | Attention | Notable Features |
|---|---|---|---|---|---|---|
| E2B | 35 | 1536 | 8 | 1 | 4:1 local:global | PLI, KV sharing, vision, audio |
| E4B | 42 | 2560 | 8 | 2 | 5:1 local:global | PLI, KV sharing, vision, audio |
| 12B | 48 | 3840 | 16 | 8 (1 global) | 5:1 local:global | Encoder-free vision + audio, K=V global |
| 31B | 60 | 5376 | 32 | 16 (4 global) | 5:1 local:global | K=V global, bidirectional vision |
| 26B-A4B | 30 | 2816 | 16 | 8 (2 global) | 5:1 local:global | MoE (128 experts, top-8), K=V global, vision |
All five use QK-norm and V-norm.
Encoder-free variants (12B)
The 12B (HF `gemma4_unified`) drops the multimodal towers entirely — there is no SigLIP-style vision encoder and no conformer:
- Vision: raw 48×48 pixel patches (3×3 groups of 16px patches merged) → LayerNorm → Linear → LayerNorm → factorised 2-D position embedding → LayerNorm → RMSNorm → Linear. The spatial pooling the tower models apply after their encoder happens here instead, before any projection.
- Audio: raw 16 kHz waveform chunked into 640-sample frames (40 ms), each frame projected by RMSNorm → Linear. No mel spectrogram.
The text decoder is the same `TransformerBlock` as every other variant. Config types are `EncoderFreeVisionConfig` / `EncoderFreeAudioConfig`; `Composer` and `preprocess_images` handle both families automatically, and the encoder-free audio path passes `audio_frames` where the tower path passes `audio_mel`.
Installation
pip install -e .
Core dependencies are `torch`, `sentencepiece`, `safetensor
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