Google Gemma-4-E4B-text-base (Modality-Stripped Text Backbone)
Gemma-4-E4B-text-base is a clean, text-only extraction of Google's Gemma-4-E4B-it instruction-tuned model. By decoupling and stripping multimodal projections (vision and audio towers), this checkpoint provides a pure language model backbone optimized for text, software engineering, and tool-calling deployments.
⚡ Why Modality-Stripped?
Standard Gemma-4 checkpoints integrate multimodal encoders and cross-attention projectors. For dedicated text generation, reasoning, and coding agent workloads:
- VRAM Reduction: Saves memory otherwise reserved for vision transformer buffers and projector parameters.
- Simplified Inference: Eliminates multimodal input pre-processing overhead and dependencies in downstream serving engines (vLLM, TensorRT-LLM, llama.cpp).
- Pristine Weight Fidelity: The underlying text transformer parameters (
model.language_model.*) are 100% identical bit-for-bit to the upstream release.
🔬 Model Profile
- Parameter Count: $\sim 4.2\text{B}$ parameters.
- Context Length: 131,072 tokens.
- Format: Standard Hugging Face
safetensorscompatible withtransformers >= 4.40.0. - Vocabulary Size: 256,000 tokens.
⚠️ Important: Tool-Calling Parser Bugfix in vLLM
- Upstream Bug Report: vllm-project/vllm#54256
- Official Pull Request: vllm-project/vllm#54257 —
[Bugfix][Parser] Support bare call: and whitespace-free channel transitions in Gemma4 parser
🚀 Serving with vLLM
vllm serve wyattearp/Gemma-4-E4B-text-base \
--host 0.0.0.0 \
--port 8000 \
--dtype bfloat16 \
--max-model-len 131072 \
--tool-call-parser gemma4 \
--reasoning-parser gemma4 \
--enable-auto-tool-choice
📄 License
Inherits upstream Gemma Terms of Use from Google DeepMind.