Sentence Similarity
GGUF
sentence-transformers
multilingual
llama.cpp
embeddings
retrieval
rag
granite
modernbert
feature-extraction
Instructions to use trithemius/granite-embedding-311m-multilingual-r2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use trithemius/granite-embedding-311m-multilingual-r2-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("trithemius/granite-embedding-311m-multilingual-r2-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 trithemius/granite-embedding-311m-multilingual-r2-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 trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16 # Run inference directly in the terminal: llama cli -hf trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16 # Run inference directly in the terminal: llama cli -hf trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16
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 trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16
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 trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16
Use Docker
docker model run hf.co/trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use trithemius/granite-embedding-311m-multilingual-r2-GGUF with Ollama:
ollama run hf.co/trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16
- Unsloth Desktop
- Docker Model Runner
How to use trithemius/granite-embedding-311m-multilingual-r2-GGUF with Docker Model Runner:
docker model run hf.co/trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16
- Lemonade
How to use trithemius/granite-embedding-311m-multilingual-r2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull trithemius/granite-embedding-311m-multilingual-r2-GGUF:F16
Run and chat with the model
lemonade run user.granite-embedding-311m-multilingual-r2-GGUF-F16
List all available models
lemonade list
- Atomic Chat
Download granite-embedding-311m-multilingual-r2-F16.gguf from trithemius/granite-embedding-311m-multilingual-r2-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 638 MB
-
https://huggingface.co/trithemius/granite-embedding-311m-multilingual-r2-GGUF/resolve/main/granite-embedding-311m-multilingual-r2-F16.gguf
- Command line
-
hf download hf://trithemius/granite-embedding-311m-multilingual-r2-GGUF/granite-embedding-311m-multilingual-r2-F16.gguf
-
curl -L -o granite-embedding-311m-multilingual-r2-F16.gguf https://huggingface.co/trithemius/granite-embedding-311m-multilingual-r2-GGUF/resolve/main/granite-embedding-311m-multilingual-r2-F16.gguf
638 MB
- Xet hash:
- 667639e044f364d3ebd2540909a7776bf3584addc4d7b8d03962007f2aa91c20
- Size of remote file:
- 638 MB
- SHA256:
- ec57b095530c2b3106c7112abc5e504b0b7588f072df69f5040f4f742342cae7
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