Initial upload: Gemma 4 vision encoder (569.6M, 27-layer ViT with 2D RoPE)

3a8691c verified 6 days ago

6.5 kB

language:
  - en
  - multilingual
license: apache-2.0
library_name: transformers
tags:
  - feature-extraction
  - image-feature-extraction
  - vision
  - vit
  - gemma4
  - google
  - safetensors
pipeline_tag: image-feature-extraction
base_model: google/gemma-4-31B-it
model-index:
  - name: gemma4-vision-encoder
    results:
      - task:
          type: image-classification
          name: CIFAR-10 (10-class)
        dataset:
          name: CIFAR-10
          type: cifar10
          split: test
        metrics:
          - type: accuracy
            value: 94
            name: Linear Probe Accuracy

Gemma 4 Vision Encoder (27-layer ViT with 2D RoPE)

Standalone extraction of the vision encoder from Google's Gemma 4 31B multimodal model. This is a 569.6M parameter Vision Transformer with learned 2D positional embeddings, RoPE, QK-norms, and gated MLP — a significant upgrade from the SigLIP encoder used in Gemma 3.

License: Apache 2.0 (inherited from Gemma 4 — no restrictions)

Architecture

Property	Value
Total parameters	569.6M
Architecture	ViT with 2D RoPE + learned positional embeddings
Hidden dimension	1152
Encoder layers	27
Attention heads	16 (72 dim per head)
KV heads	16 (full MHA, no GQA)
MLP	Gated (gate_proj + up_proj + down_proj)
MLP intermediate	4304
Activation	GELU (pytorch_tanh variant)
Normalization	RMSNorm (eps=1e-6)
Patch size	16×16
Pooling	3×3 kernel (reduces token count by 9×)
Position embeddings	Learned 2D table (2, 10240, 1152) + RoPE (theta=100)
Q/K norms	Yes
Default output tokens	280
Configurable token budgets	70, 140, 280, 560, 1120
Input	Pre-patchified: `(batch, num_patches, 768)` where 768 = 3×16×16
Output	`(num_valid_tokens, 1152)` after pooling + standardization

What's New vs Gemma 3 (SigLIP)

	Gemma 3 Vision	Gemma 4 Vision (this model)
Architecture	SigLIP (ViT-SO400M)	Custom ViT with 2D RoPE
Layers	27	27
Hidden dim	1152	1152
Position encoding	Learned 1D	Learned 2D + RoPE
Attention	Standard	QK-normed
MLP	Standard (fc1 + fc2)	Gated (gate + up + down)
Aspect ratio	Fixed square (896×896)	Variable aspect ratio
Token budget	Fixed 256	Configurable (70–1120)
Pooling	4×4 average	3×3

Not Shared with E2B/E4B

Unlike the audio encoder (which is identical across E2B and E4B), the vision encoders differ:

	E2B/E4B	31B (this extraction)
Layers	16	27
Parameters	~340M	569.6M

Usage

import torch
from transformers import Gemma4VisionModel, Gemma4ImageProcessor
from PIL import Image

# Load vision encoder directly from this repo
vision_model = Gemma4VisionModel.from_pretrained(
    "rnagabh/gemma4-vision-encoder",
    torch_dtype=torch.bfloat16,
)
vision_model.to("cuda")
vision_model.eval()

# Load image processor (saved in this repo)
image_processor = Gemma4ImageProcessor.from_pretrained("rnagabh/gemma4-vision-encoder")

# Process an image
img = Image.open("your_image.jpg")
processed = image_processor(images=[img], return_tensors="pt")

pixel_values = processed["pixel_values"].to(dtype=torch.bfloat16, device="cuda")
position_ids = processed["image_position_ids"].to(device="cuda")
tokens_per_image = processed["num_soft_tokens_per_image"]  # for splitting batch output

with torch.no_grad():
    output = vision_model(pixel_values=pixel_values, pixel_position_ids=position_ids)
    embeddings = output.last_hidden_state  # (num_tokens, 1152)

    # Mean-pool for a single image vector
    image_embedding = embeddings.float().mean(dim=0)  # (1152,)

Important: Always use the Gemma4ImageProcessor included in this repo for preprocessing. It handles resizing, patchification, position ID generation, and pixel normalization. Manual patchification without this processor will produce significantly degraded results.

Benchmark Results (frozen 1152-dim embeddings, linear probe)

CIFAR-10 Classification

Metric	Value
Linear probe accuracy	94.0%
Random baseline	10.0%
Improvement over chance	9.4×
Dataset	CIFAR-10 test set (1000 samples, 100 per class)
Probe	Logistic regression on L2-normalized mean-pooled embeddings

Strong performance across all classes: airplane (0.98 F1), ship (0.98 F1), truck (0.97 F1), automobile (0.97 F1). Weakest class is cat (0.86 F1) — a fine-grained category that is inherently harder.

Files in This Repo

File	Description	Size
`config.json`	Vision encoder config (Gemma4VisionConfig)	<1 KB
`model.safetensors`	Vision encoder weights (569.6M params, BF16)	1,139 MB
`preprocessor_config.json`	Image processor config (Gemma4ImageProcessor)	<1 KB
`embed_vision.safetensors`	Vision→text embedding projection (1152→5376)	12.4 MB

Limitations

End-to-end trained for LLM decoding: The encoder was trained to produce features for Gemma 4's text decoder. The 1152-dim output is the pure vision representation; the embed_vision projection maps to the 31B's text hidden space (5376-dim).
Requires image processor: Use the Gemma4ImageProcessor included in this repo for preprocessing. The model expects pre-patchified (B, num_patches, 768) tensors with explicit 2D position IDs — the processor handles this automatically.
Variable aspect ratio support: The 2D position embeddings enable non-square images. The processor generates correct position IDs for any aspect ratio.
Output shape note: The pooler strips padding and collapses the batch dimension, returning (num_valid_tokens, 1152). For batched inference, use num_soft_tokens_per_image from the processor to split the output back into per-image embeddings.

Extraction Details

Extracted from google/gemma-4-31B-it by downloading only the shard containing vision tower weights (model-00001-of-00002.safetensors)
No full model load required — targeted tensor extraction
Weights loaded with strict=True — perfect match
Forward pass verified: 864×864 image → (324, 1152) output
All architecture specs verified against the live model config

rnagabh
/

gemma4-vision-encoder