| ---
|
| license: apache-2.0
|
| base_model: google/embeddinggemma-2
|
| pipeline_tag: feature-extraction
|
| library_name: lm-kit
|
| tags:
|
| - embedding
|
| - sentence-similarity
|
| - multimodal-embedding
|
| - lmk
|
| - gguf
|
| - lm-kit
|
| language:
|
| - multilingual
|
| ---
|
|
|
| # EmbeddingGemma 2 (LMK) for LM-Kit.NET
|
|
|
| An LM-Kit model archive of [google/embeddinggemma-2](https://huggingface.co/google/embeddinggemma-2), the
|
| open multimodal embedding model from Google DeepMind, packaged for
|
| [LM-Kit.NET](https://lm-kit.com). Text, code and images are embedded into one 768-dimensional vector
|
| space (Matryoshka truncation to 512, 256 or 128 dimensions, re-normalized), with an 8,192-token
|
| context.
|
|
|
| | File | Content | Size |
|
| |---|---|---|
|
| | `embeddinggemma-2-Q5_K_M.lmk` | LM-Kit archive: the text encoder (Q5_K_M) and the vision encoder (Q8_0) in one file. This is what `LM.LoadFromModelID("embeddinggemma-2")` loads | 439 MB |
|
| | `embeddinggemma-2-Q5_K_M.gguf` | Text encoder (270M parameters), Q5_K_M | 213 MB |
|
| | `embeddinggemma-2-mmproj-vision-Q8_0.gguf` | Vision encoder (170M parameters), Q8_0, audio tower removed | 226 MB |
|
| | `embeddinggemma-2-mmproj-vision-BF16.gguf` | Vision encoder, BF16 reference (full precision, for re-quantization) | 368 MB |
|
|
|
| The archive's vision encoder reads only its header at load: its weights reach memory with the first image, so text-only use costs no vision memory. The GGUF files are the archive's contents, for runtimes that load GGUF directly.
|
|
|
| ## Usage
|
|
|
| ```csharp
|
| using LMKit.Model;
|
| using LMKit.Embeddings;
|
| using LMKit.Media.Image;
|
|
|
| // Text, code and images, in the same vector space
|
| using LM model = LM.LoadFromModelID("embeddinggemma-2");
|
| var embedder = new Embedder(model);
|
|
|
| float[] query = embedder.GetQueryEmbeddings("waterproof running shoes");
|
| float[] document = embedder.GetEmbeddings("Our trail shoe keeps your feet dry on wet rock.");
|
| float[] image = embedder.GetEmbeddings(ImageBuffer.LoadAsRGB("shoe.jpg"));
|
| ```
|
|
|
| LM-Kit.NET applies the model card's prompts by retrieval role (`task: search result | query: ` for
|
| queries, `title: none | text: ` for documents), frames every input with `<bos>` and `<eos>` as the
|
| publisher's tokenizer does, and sizes images with the model's own processor rule (280 soft tokens
|
| by default, aspect ratio preserved, bicubic resampling).
|
|
|
| ## Conversion
|
|
|
| Converted with LM-Kit's llama.cpp fork (upstream `convert_hf_to_gguf.py`, architecture
|
| `gemma-embedding2`, projector `gemma4v`). Every text tokenizes to exactly the publisher's token
|
| ids, and the vectors match the publisher's own pipeline (sentence-transformers) for text and
|
| images.
|
|
|
| ## License
|
|
|
| Apache 2.0, as the original model. See the
|
| [Gemma 4 license](https://ai.google.dev/gemma/docs/gemma_4_license) and the
|
| [original model card](https://huggingface.co/google/embeddinggemma-2).
|
|
|
