Add Embedding Gemma model card with Q8_0 quantization and Unsloth attribution

ai/embedding-gemma.md

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+# Embedding Gemma
+
+![logo](https://github.com/docker/model-cards/raw/refs/heads/main/logos/[email protected])
+
+**Embedding Gemma** is a state-of-the-art text embedding model from Google DeepMind, designed to create high-quality vector representations of text. Built on the Gemma architecture, this model converts text into dense vector embeddings that capture semantic meaning, making it ideal for retrieval-augmented generation (RAG), semantic search, and similarity tasks. With open weights and efficient design, Embedding Gemma provides a powerful foundation for embedding-based applications. The GGUF format version is provided by Unsloth.
+
+## Intended uses
+
+Embedding Gemma is designed for applications requiring high-quality text embeddings:
+
+- **Semantic search and retrieval**: Excellent for building search systems, document retrieval, and RAG applications that need to find semantically relevant content.
+- **Text similarity and clustering**: Generate embeddings for measuring text similarity, document clustering, and content deduplication tasks.
+- **Classification and downstream tasks**: Use embeddings as input features for various NLP classification tasks and machine learning pipelines.
+
+## Characteristics
+
+| Attribute             | Details                                                      |
+|---------------------- |--------------------------------------------------------------|
+| **Provider**          | Google DeepMind                                              |
+| **Architecture**      | Gemma Embedding                                              |
+| **Cutoff date**       | -                                                            |
+| **Languages**         | English                                                      |
+| **Tool calling**      | ❌                                                           |
+| **Input modalities**  | Text                                                         |
+| **Output modalities** | Embedding vectors                                            |
+| **License**           | [Gemma Terms](https://ai.google.dev/gemma/terms)            |
+
+## Available model variants
+
+| Model variant                                                        | Parameters | Quantization | Context window | VRAM¹    | Size      |
+|----------------------------------------------------------------------|------------|--------------|----------------|----------|-----------|
+| `ai/embedding-gemma:latest`<br><br>`ai/embedding-gemma:300M-Q8_0`    | 300M       | Q8_0         | 2K tokens      | 0.95 GiB | 761.25 MB |
+| `ai/embedding-gemma:300M-Q8_0`                                       | 300M       | Q8_0         | 2K tokens      | 0.95 GiB | 761.25 MB |
+
+¹: VRAM estimated based on model characteristics.
+
+> `latest` → `300M-Q8_0`
+
+## Use this AI model with Docker Model Runner
+
+First, pull the model:
+
+```bash
+docker model pull ai/embedding-gemma
+```
+
+Then run the model:
+
+```bash
+docker model run ai/embedding-gemma
+```
+
+To generate embeddings using the API:
+
+```bash
+curl --location 'http://localhost:12434/engines/llama.cpp/v1/embeddings' \
+--header 'Content-Type: application/json' \
+--data '{
+    "model": "ai/embedding-gemma",
+    "input": "Your text to embed here"
+  }'
+```
+
+For more information on Docker Model Runner, [explore the documentation](https://docs.docker.com/desktop/features/model-runner/).
+
+## Considerations
+
+- **Context length**: The model supports up to 2K tokens. Longer texts may need to be chunked for optimal performance.
+- **Language support**: Primarily trained on English text, performance on other languages may vary.
+- **Embedding dimension**: The model produces 768-dimensional embeddings suitable for most downstream tasks.
+- **Normalization**: Embeddings are normalized by default, making them suitable for cosine similarity calculations.
+
+## Benchmark performance
+
+| Task Category       | Embedding Gemma |
+|---------------------|----------------|
+| Retrieval          | 54.87          |
+| STS                | 78.53          |
+| Classification     | 73.26          |
+| Clustering         | 44.72          |
+| Pair Classification| 85.94          |
+| Reranking          | 59.36          |
+
+## Links
+
+- [Embedding Gemma Model Card](https://huggingface.co/google/embeddinggemma-300m)
+- [Unsloth GGUF Version](https://huggingface.co/unsloth/embeddinggemma-300m-GGUF)
+- [Gemma Model Family](https://ai.google.dev/gemma/docs)
+- [Gemma Terms of Use](https://ai.google.dev/gemma/terms)