Sentence Similarity
sentence-transformers
ONNX
Safetensors
GGUF
English
embedding_gemma2
feature-extraction
embeddings
retrieval
e-commerce
product-search
shopping
matryoshka
embeddinggemma
Instructions to use smshahbaj/embeddinggemma-2-ecommerce with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use smshahbaj/embeddinggemma-2-ecommerce with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("smshahbaj/embeddinggemma-2-ecommerce") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use smshahbaj/embeddinggemma-2-ecommerce with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M --embeddings
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M --embeddings
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible embeddings server: ./build/bin/llama-server -hf smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use smshahbaj/embeddinggemma-2-ecommerce with Ollama:
ollama run hf.co/smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use smshahbaj/embeddinggemma-2-ecommerce with Docker Model Runner:
docker model run hf.co/smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M
- Lemonade
How to use smshahbaj/embeddinggemma-2-ecommerce with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M
Run and chat with the model
lemonade run user.embeddinggemma-2-ecommerce-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download processor_config.json from smshahbaj/embeddinggemma-2-ecommerce: direct link, hf CLI and curl.
- Browser
- Download file 1.79 kB
-
https://huggingface.co/smshahbaj/embeddinggemma-2-ecommerce/resolve/main/processor_config.json
- Command line
-
hf download hf://smshahbaj/embeddinggemma-2-ecommerce/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/smshahbaj/embeddinggemma-2-ecommerce/resolve/main/processor_config.json
1.79 kB
| { | |
| "audio_ms_per_token": 40, | |
| "audio_seq_length": 280, | |
| "feature_extractor": { | |
| "dither": 0.0, | |
| "feature_extractor_type": "Gemma4AudioFeatureExtractor", | |
| "feature_size": 128, | |
| "fft_length": 512, | |
| "fft_overdrive": false, | |
| "frame_length": 320, | |
| "hop_length": 160, | |
| "input_scale_factor": 1.0, | |
| "max_frequency": 8000.0, | |
| "mel_floor": 0.001, | |
| "min_frequency": 0.0, | |
| "padding_side": "right", | |
| "padding_value": 0.0, | |
| "per_bin_mean": null, | |
| "per_bin_stddev": null, | |
| "preemphasis": 0.0, | |
| "preemphasis_htk_flavor": true, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| }, | |
| "image_processor": { | |
| "do_convert_rgb": true, | |
| "do_normalize": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_processor_type": "Gemma4ImageProcessor", | |
| "image_seq_length": 280, | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_soft_tokens": 280, | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098 | |
| }, | |
| "image_seq_length": 280, | |
| "processor_class": "EmbeddingGemma2Processor", | |
| "video_processor": { | |
| "add_timestamps": false, | |
| "do_convert_rgb": true, | |
| "do_normalize": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "do_sample_frames": true, | |
| "fps": 1, | |
| "image_mean": [ | |
| 0.0, | |
| 0.0, | |
| 0.0 | |
| ], | |
| "image_std": [ | |
| 1.0, | |
| 1.0, | |
| 1.0 | |
| ], | |
| "max_frames": 32, | |
| "max_soft_tokens": 140, | |
| "overflow_strategy": "uniform", | |
| "patch_size": 16, | |
| "pooling_kernel_size": 3, | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_metadata": false, | |
| "video_processor_type": "EmbeddingGemma2VideoProcessor" | |
| } | |
| } | |