- Libraries
- sentence-transformers
How to use KBLab/sentence-bert-swedish-cased with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("KBLab/sentence-bert-swedish-cased")
sentences = [
"Mannen åt mat.",
"Han förtärde en närande och nyttig måltid.",
"Det var ett sunkigt hak med ganska gott käk.",
"Han inmundigade middagen tillsammans med ett glas rödvin.",
"Potatischips är jättegoda.",
"Tryck på knappen för att få tala med kundsupporten."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [6, 6] - Transformers
How to use KBLab/sentence-bert-swedish-cased with Transformers:
# Load model directly
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("KBLab/sentence-bert-swedish-cased")
model = AutoModel.from_pretrained("KBLab/sentence-bert-swedish-cased") - llama-cpp-python
How to use KBLab/sentence-bert-swedish-cased with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="KBLab/sentence-bert-swedish-cased",
filename="Sentence-Bert-Swedish-Cased-124M-BF16.gguf",
)
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output)
- Inference
-
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use KBLab/sentence-bert-swedish-cased with llama.cpp:
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf KBLab/sentence-bert-swedish-cased:BF16
# Run inference directly in the terminal:
llama-cli -hf KBLab/sentence-bert-swedish-cased:BF16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf KBLab/sentence-bert-swedish-cased:BF16
# Run inference directly in the terminal:
llama-cli -hf KBLab/sentence-bert-swedish-cased:BF16
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 KBLab/sentence-bert-swedish-cased:BF16
# Run inference directly in the terminal:
./llama-cli -hf KBLab/sentence-bert-swedish-cased:BF16
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 KBLab/sentence-bert-swedish-cased:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KBLab/sentence-bert-swedish-cased:BF16
Use Docker
docker model run hf.co/KBLab/sentence-bert-swedish-cased:BF16
- LM Studio
- Jan
- Ollama
How to use KBLab/sentence-bert-swedish-cased with Ollama:
ollama run hf.co/KBLab/sentence-bert-swedish-cased:BF16
- Unsloth Studio new
How to use KBLab/sentence-bert-swedish-cased with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for KBLab/sentence-bert-swedish-cased to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for KBLab/sentence-bert-swedish-cased to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KBLab/sentence-bert-swedish-cased to start chatting
- Docker Model Runner
How to use KBLab/sentence-bert-swedish-cased with Docker Model Runner:
docker model run hf.co/KBLab/sentence-bert-swedish-cased:BF16
- Lemonade
How to use KBLab/sentence-bert-swedish-cased with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull KBLab/sentence-bert-swedish-cased:BF16
Run and chat with the model
lemonade run user.sentence-bert-swedish-cased-BF16
List all available models
lemonade list
Cablecutter