jmorganca cd2dcaff49 llama/compat: add embeddinggemma handler
Ollama publishes embeddinggemma:300m with general.architecture=gemma3
plus two extra dense projection layers stored as `dense.0.weight` /
`dense.1.weight` (the sentence-transformers post-pooling head, mapping
the 768-dim pooled embedding through 768→3072→768).

Upstream loads this model under arch=gemma-embedding, which both
disables causal attention (embeddings are bidirectional) and loads
`dense_2`/`dense_3` by name from the gemma-embedding.dense_*_feat_*
hparams. Without that arch, the gemma3 loader leaves dense.0/dense.1
unrequested and `done_getting_tensors` raises "wrong number of
tensors; expected 316, got 314".

Detection: arch=gemma3 AND has dense.0.weight tensor (only
embeddinggemma ships these — regular gemma3 chat models do not, so
the existing handle_gemma3 path remains for them).

Translation:
  * switch arch_name to gemma-embedding (used by the loader to build
    KV lookup prefix for subsequent reads)
  * copy gemma3.* KV prefix to gemma-embedding.*
  * derive gemma-embedding.dense_{2,3}_feat_{in,out} from the actual
    dense tensor shapes
  * rename dense.0/dense.1 → dense_2/dense_3

Verified via /api/embed: returns 768-dim vectors with correct cosine
similarity ordering (similar sentences cluster, dissimilar ones don't).
Confirmed gemma3:1b chat path is unaffected — handle_embeddinggemma's
detection only matches blobs with the dense.0 tensor present.
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curl http://localhost:11434/api/chat -d '{
  "model": "gemma3",
  "messages": [{
    "role": "user",
    "content": "Why is the sky blue?"
  }],
  "stream": false
}'

See the API documentation for all endpoints.

Python

pip install ollama
from ollama import chat

response = chat(model='gemma3', messages=[
  {
    'role': 'user',
    'content': 'Why is the sky blue?',
  },
])
print(response.message.content)

JavaScript

npm i ollama
import ollama from "ollama";

const response = await ollama.chat({
  model: "gemma3",
  messages: [{ role: "user", content: "Why is the sky blue?" }],
});
console.log(response.message.content);

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