Feature Extraction
GGUF
sentence-transformers
llama.cpp
embedding
multimodal
multilingual
quantized
imatrix
conversational
Instructions to use ngquocvinh/EmbeddingGemma-2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ngquocvinh/EmbeddingGemma-2-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ngquocvinh/EmbeddingGemma-2-GGUF") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ngquocvinh/EmbeddingGemma-2-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M
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 server with a web UI: ./build/bin/llama-server -hf ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M
Use Docker
docker model run hf.co/ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use ngquocvinh/EmbeddingGemma-2-GGUF with Ollama:
ollama run hf.co/ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use ngquocvinh/EmbeddingGemma-2-GGUF with Docker Model Runner:
docker model run hf.co/ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M
- Lemonade
How to use ngquocvinh/EmbeddingGemma-2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ngquocvinh/EmbeddingGemma-2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.EmbeddingGemma-2-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Add Q3_K_M after CPU embedding smoke pass
Browse files- .gitattributes +1 -0
- EmbeddingGemma-2-Q3_K_M.gguf +3 -0
- SHA256SUMS.txt +1 -0
- reproducibility/artifact-measurements.tsv +1 -0
- reproducibility/manifest.md +8 -0
.gitattributes
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EmbeddingGemma-2-Q3_K_M.gguf
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size 148869760
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ea25550b050fa8826707c5e44659a7c18259606dcc6ba449f771e3c75cf4e663 EmbeddingGemma-2-Q4_K_M.gguf
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22f79226a02bfa2dd840414f342a004f93b3cb85e498923a5e089f03f6410b1f EmbeddingGemma-2-Q5_K_M.gguf
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a5c902b15458c4886d6f1296bad71fd8084d7ac626cb8713778cf2af5e1b5a71 EmbeddingGemma-2-Q3_K_M.gguf
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ea25550b050fa8826707c5e44659a7c18259606dcc6ba449f771e3c75cf4e663 EmbeddingGemma-2-Q4_K_M.gguf
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22f79226a02bfa2dd840414f342a004f93b3cb85e498923a5e089f03f6410b1f EmbeddingGemma-2-Q5_K_M.gguf
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21693ca41877ffb20727763e167c503b0467eac4ccef51551230d72c2afcd37e EmbeddingGemma-2-Q6_K.gguf
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reproducibility/artifact-measurements.tsv
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EmbeddingGemma-2-Q6_K.gguf 245764736 0.245765 PASS_EMBEDDING_CPU
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- Quantization command: `llama-quantize --imatrix calibration/embeddinggemma-2/embeddinggemma-2-sts.imatrix.gguf embeddinggemma-2-text-BF16.gguf EmbeddingGemma-2-Q4_K_M.gguf Q4_K_M 8`.
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- CPU `llama-server --embedding --pooling mean -ngl 0` smoke passed with two finite normalized 768-dimensional text vectors.
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- Build cgroup: `MemoryMax=32G`, `MemorySwapMax=0`; batch peak memory `930787328` bytes and swap peak `0` bytes.
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- Quantization command: `llama-quantize --imatrix calibration/embeddinggemma-2/embeddinggemma-2-sts.imatrix.gguf embeddinggemma-2-text-BF16.gguf EmbeddingGemma-2-Q4_K_M.gguf Q4_K_M 8`.
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- CPU `llama-server --embedding --pooling mean -ngl 0` smoke passed with two finite normalized 768-dimensional text vectors.
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- Build cgroup: `MemoryMax=32G`, `MemorySwapMax=0`; batch peak memory `930787328` bytes and swap peak `0` bytes.
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- Q4_K_M public Hub commit: `81f6dddee3ab68d581201a40a8e739b37b06d829`; remote LFS SHA256 and size match the local file.
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## Incremental publish: Q3_K_M
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- Artifact: `EmbeddingGemma-2-Q3_K_M.gguf`; SHA256 `a5c902b15458c4886d6f1296bad71fd8084d7ac626cb8713778cf2af5e1b5a71`; decimal size `0.148870 GB`.
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- Quantization command: `llama-quantize --imatrix calibration/embeddinggemma-2/embeddinggemma-2-sts.imatrix.gguf embeddinggemma-2-text-BF16.gguf EmbeddingGemma-2-Q3_K_M.gguf Q3_K_M 8`.
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- CPU `llama-server --embedding --pooling mean -ngl 0` smoke passed with two finite normalized 768-dimensional text vectors.
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- Build cgroup: `MemoryMax=32G`, `MemorySwapMax=0`; batch peak memory `878006272` bytes and swap peak `0` bytes.
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