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154c2d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 | # EmbeddingGemma: multimodal embeddings
This page is the MIC integration contract for `google/embeddinggemma-2`. It shows the public `transformers` API for computing text and image embeddings and for running a short training step on the model's own parameters. The `runnable` blocks below are rendered by doc-builder and executed by the MIC runner against the slim fixture and the pinned checkpoint. Lines marked `# doc-builder: hide` are runner plumbing and are not shown in the rendered documentation.
## Compute text and image embeddings
```py runnable:embed
import torch
from transformers import AutoModel, AutoProcessor, set_seed
import json # doc-builder: hide
import os # doc-builder: hide
model_id = "google/embeddinggemma-2"
model_id = os.environ.get("MIC_MODEL", model_id) # doc-builder: hide
variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}")) # doc-builder: hide
device = "cuda" if torch.cuda.is_available() else "cpu"
set_seed(0) # reproducible synthetic inputs and dropout
processor = AutoProcessor.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id).to(device)
if variant: # doc-builder: hide
del model # doc-builder: hide
model = AutoModel.from_pretrained(model_id, **variant).to(device) # doc-builder: hide
model.eval()
texts = [
"What causes the northern lights?",
"Charged particles from the sun cause the northern lights.",
]
# Two small synthetic RGB images; the processor resizes and normalizes them.
images = [torch.rand(3, 224, 224) for _ in range(2)]
text_inputs = processor(text=texts, return_tensors="pt", padding=True)
text_inputs = text_inputs.to(device)
# The processor packs a list of images into one sequence: embed each on its own.
image_inputs = [processor(images=[image], return_tensors="pt").to(device, dtype=model.dtype) for image in images]
with torch.no_grad():
text_out = model(**text_inputs)
image_outs = [model(**inputs) for inputs in image_inputs]
# EmbeddingGemma2Model returns hidden states; mean-pool them into embeddings.
def mean_pool(last_hidden_state, attention_mask):
mask = attention_mask.unsqueeze(-1).to(last_hidden_state.dtype)
return (last_hidden_state * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9)
text_emb = mean_pool(text_out.last_hidden_state, text_inputs["attention_mask"])
image_emb = torch.cat([out.last_hidden_state.mean(dim=1) for out in image_outs]) # image tokens are never padded
similarity = torch.nn.functional.cosine_similarity(text_emb[0], text_emb[1], dim=0)
print(f"similarity: {similarity.item():.4f}")
```
## One training step
```py runnable:embed:2
# In-batch contrastive step: each text should match its own image. The embeddings
# are recomputed with gradients (the ones above were taken under no_grad).
model.train()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-4)
text_train = mean_pool(model(**text_inputs).last_hidden_state, text_inputs["attention_mask"])
image_train = torch.cat([model(**inputs).last_hidden_state.mean(dim=1) for inputs in image_inputs])
scores = torch.nn.functional.normalize(text_train.float(), dim=-1) @ torch.nn.functional.normalize(image_train.float(), dim=-1).T
loss = torch.nn.functional.cross_entropy(scores / 0.05, torch.arange(len(scores), device=device))
loss.backward()
before = {n: p.detach().cpu().clone() for n, p in model.named_parameters() if p.requires_grad} # doc-builder: hide
optimizer.step()
print(f"loss: {loss.item():.4f}")
if os.environ.get("MIC_RUNNER"): # doc-builder: hide
named = dict(model.named_parameters())
checked = (
"language_model.layers.0.self_attn.q_proj.weight",
"language_model.layers.0.mlp.down_proj.weight",
"vision_tower.encoder.layers.0.self_attn.q_proj.linear.weight",
)
result = {
"runtime": {
"device": model.device.type,
"dtype": str(model.dtype).removeprefix("torch."),
"attn_implementation": model.config._attn_implementation,
},
"text_embeddings": text_emb.float().tolist(),
"image_embeddings": image_emb.float().tolist(),
"similarity": similarity.item(),
"training": {
"loss": loss.item(),
"gradients": {
n: bool(
named[n].grad is not None
and torch.count_nonzero(named[n].grad) > 0
and torch.isfinite(named[n].grad).all()
)
for n in checked
},
"updated": any(
not torch.equal(p.detach().cpu(), before[n])
for n, p in model.named_parameters()
if n in before
),
},
}
print(json.dumps(result))
```
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