Instructions to use kalle07/embedder_collection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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 Settings

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

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

kalle07 commited on Mar 9, 2025

Commit

414bd9c

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Update README.md

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@@ -89,31 +89,31 @@ on discord (sevenof9)
 ...
-- avemio/German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI (German, English)
 - maidalun1020/bce-embedding-base_v1 (English and Chinese) - only ~200pages
-- maidalun1020/bce-reranker-base_v1 (English, Chinese, Japanese and Korean)
 - BAAI/bge-reranker-v2-m3 (English and Chinese) - 600pages and more
-- BAAI/bge-reranker-v2-gemma (English and Chinese)
 - BAAI/bge-m3 (English and Chinese) - 600pages and more
-- avsolatorio/GIST-large-Embedding-v0 (English)
-- ibm-granite/granite-embedding-278m-multilingual (English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese)
-- Labib11/MUG-B-1.6 (?)
-- mixedbread-ai/mxbai-embed-large-v1 (multi)
 - nomic-ai/nomic-embed-text-v1.5 (English, multi) - 600pages and more
-- Snowflake/snowflake-arctic-embed-l-v2.0 (English, multi)
-- intfloat/multilingual-e5-large-instruct (100 languages)

 ...
+- avemio/German-RAG-BGE-M3-MERGED-x-SNOWFLAKE-ARCTIC-HESSIAN-AI (German, English) - 600pages and more
 - maidalun1020/bce-embedding-base_v1 (English and Chinese) - only ~200pages
+- maidalun1020/bce-reranker-base_v1 (English, Chinese, Japanese and Korean) - only ~200pages
 - BAAI/bge-reranker-v2-m3 (English and Chinese) - 600pages and more
+- BAAI/bge-reranker-v2-gemma (English and Chinese) - dont work
 - BAAI/bge-m3 (English and Chinese) - 600pages and more
+- avsolatorio/GIST-large-Embedding-v0 (English)- ~300pages
+- ibm-granite/granite-embedding-278m-multilingual (English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese) - only ~200pages
+- Labib11/MUG-B-1.6 (?) - ~300pages
+- mixedbread-ai/mxbai-embed-large-v1 (multi) - ~300pages
 - nomic-ai/nomic-embed-text-v1.5 (English, multi) - 600pages and more
+- Snowflake/snowflake-arctic-embed-l-v2.0 (English, multi) - 600pages and more
+- intfloat/multilingual-e5-large-instruct (100 languages) - only ~200pages