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
File size: 1,788 Bytes
cfda0b0 | 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 | {
"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"
}
}
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