Sentence Similarity
sentence-transformers
GGUF
mteb
feature-extraction
dataset_size:100K<n<1M
loss:AnglELoss
autoquant
Eval Results (legacy)
Instructions to use MesTruck/STS-multilingual-mpnet-base-v2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use MesTruck/STS-multilingual-mpnet-base-v2-GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("MesTruck/STS-multilingual-mpnet-base-v2-GGUF") sentences = [ "有些人在路上溜达。", "Folk går", "Otururken gitar çalan adam.", "ארה\"ב קבעה שסוריה השתמשה בנשק כימי" ] 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 MesTruck/STS-multilingual-mpnet-base-v2-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 MesTruck/STS-multilingual-mpnet-base-v2-GGUF:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf MesTruck/STS-multilingual-mpnet-base-v2-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 MesTruck/STS-multilingual-mpnet-base-v2-GGUF:Q4_K_M --embeddings
- Llama new
- LM Studio
- Jan
- Ollama
How to use MesTruck/STS-multilingual-mpnet-base-v2-GGUF with Ollama:
ollama run hf.co/MesTruck/STS-multilingual-mpnet-base-v2-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use MesTruck/STS-multilingual-mpnet-base-v2-GGUF with Docker Model Runner:
docker model run hf.co/MesTruck/STS-multilingual-mpnet-base-v2-GGUF:Q4_K_M
- Lemonade
How to use MesTruck/STS-multilingual-mpnet-base-v2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MesTruck/STS-multilingual-mpnet-base-v2-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.STS-multilingual-mpnet-base-v2-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download sts-multilingual-mpnet-base-v2.Q5_K_M.gguf from MesTruck/STS-multilingual-mpnet-base-v2-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 227 MB
-
https://huggingface.co/MesTruck/STS-multilingual-mpnet-base-v2-GGUF/resolve/main/sts-multilingual-mpnet-base-v2.Q5_K_M.gguf
- Command line
-
hf download hf://MesTruck/STS-multilingual-mpnet-base-v2-GGUF/sts-multilingual-mpnet-base-v2.Q5_K_M.gguf
-
curl -L -o sts-multilingual-mpnet-base-v2.Q5_K_M.gguf https://huggingface.co/MesTruck/STS-multilingual-mpnet-base-v2-GGUF/resolve/main/sts-multilingual-mpnet-base-v2.Q5_K_M.gguf
227 MB
- Xet hash:
- 3db0afbb95a03ce5cddfab2bef11f08bc38ebf226ad2d27c7dd7101b4488c1ef
- Size of remote file:
- 227 MB
- SHA256:
- 74d720cd704b59dd8208b3aa72b32bf044f6a1af6aed468b62d72e5ae8c2f4c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.