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]

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kalle07/embedder_collection 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 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 serve -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

Atomic Chat new
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 Feb 14

Commit

fad1bcb

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@@ -45,7 +45,7 @@ to see all files<br>
 <b> the setup for local documents described below is allmost the same, GPT4All has only one model (nomic), and koboldcpp and JAN(Menlo) is not build in right now but in development</b><br>
 (sometimes the results are more truthful if the “chat with document only” option is used)<br>
-BTW the embedder-model is only a part of a good RAG (Retrieval-Augmented Generation)<br>
 <b>&#x21e8;</b> give me a ❤️, if you like  ;)<br>
 <br>
 <b>My short impression:</b>

 <b> the setup for local documents described below is allmost the same, GPT4All has only one model (nomic), and koboldcpp and JAN(Menlo) is not build in right now but in development</b><br>
 (sometimes the results are more truthful if the “chat with document only” option is used)<br>
+Incidentally, the Embedder model is only one part of a good RAG (Retrieval-Augmented Generation), but it should be tailored to your language and, if you want it to be completely accurate, also to the application, e.g. programming or medicine. <br>
 <b>&#x21e8;</b> give me a ❤️, if you like  ;)<br>
 <br>
 <b>My short impression:</b>