Feature Extraction
GGUF
llama.cpp
embeddings
sentence-similarity
multimodal-embedding
gemma
imatrix
quantized
atomic-chat
Instructions to use AtomicChat/embeddinggemma-2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AtomicChat/embeddinggemma-2-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf AtomicChat/embeddinggemma-2-GGUF:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf AtomicChat/embeddinggemma-2-GGUF:Q4_K_M --embeddings
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible embeddings server: ./build/bin/llama serve -hf AtomicChat/embeddinggemma-2-GGUF:Q4_K_M --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use AtomicChat/embeddinggemma-2-GGUF with Ollama:
ollama run hf.co/AtomicChat/embeddinggemma-2-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use AtomicChat/embeddinggemma-2-GGUF with Docker Model Runner:
docker model run hf.co/AtomicChat/embeddinggemma-2-GGUF:Q4_K_M
- Lemonade
How to use AtomicChat/embeddinggemma-2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AtomicChat/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
Card: quality-vs-size chart with Unsloth, AutoRound and ggml-org in the header
Browse files- .gitattributes +1 -0
- README.md +3 -1
- quality-vs-size-all.png +3 -0
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mmproj-embeddinggemma-2-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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At the same size our files' vectors stay closest to the original's: 17% closer
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than the next best file at about 245 MB, and 11% closer at about 175 MB. The
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harness, the same eval set and the same machine. `1 - cosine` is the mean
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distance of a file's vectors from the original's: lower is better.
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| File | Size | same top result | 1 - cosine, 30 languages | 1 - cosine, code |
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| Unsloth `UD-Q6_K_XL` | 249 MB | 97.0% | 0.00080 | 0.00061 |
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quality-vs-size-all.png
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