Sentence Similarity
sentence-transformers
GGUF
English
German
feature-extraction
embedder
embedding
models
GGUF
Bert
Nomic
Gist
Granite
BGE
Jina
gemma
Snowflake
Qwen
text-embeddings-inference
RAG
Rerank
similarity
PDF
Parsing
Parser
Instructions to use kalle07/embedder_collection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use kalle07/embedder_collection with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kalle07/embedder_collection") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - llama-cpp-python
How to use kalle07/embedder_collection with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="kalle07/embedder_collection", filename="German-RAG-BGE-M3-TRIPLES-HESSIAN-AI.f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use kalle07/embedder_collection with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf kalle07/embedder_collection:Q4_K_M # Run inference directly in the terminal: llama-cli -hf kalle07/embedder_collection:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf kalle07/embedder_collection:Q4_K_M # Run inference directly in the terminal: llama-cli -hf kalle07/embedder_collection: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 kalle07/embedder_collection:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf kalle07/embedder_collection: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 kalle07/embedder_collection:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kalle07/embedder_collection:Q4_K_M
Use Docker
docker model run hf.co/kalle07/embedder_collection:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use kalle07/embedder_collection with Ollama:
ollama run hf.co/kalle07/embedder_collection:Q4_K_M
- Unsloth Studio new
How to use kalle07/embedder_collection 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 kalle07/embedder_collection 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 kalle07/embedder_collection to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kalle07/embedder_collection to start chatting
- Docker Model Runner
How to use kalle07/embedder_collection with Docker Model Runner:
docker model run hf.co/kalle07/embedder_collection:Q4_K_M
- Lemonade
How to use kalle07/embedder_collection with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kalle07/embedder_collection:Q4_K_M
Run and chat with the model
lemonade run user.embedder_collection-Q4_K_M
List all available models
lemonade list
Upload jina-embeddings-v2-base-en-f16.gguf
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