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 Desktop
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

Atomic Chat

kalle07 commited on Jul 12, 2025

Commit

5802c95

verified ·

1 Parent(s): a0e81dc

Update README.md

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@@ -117,7 +117,8 @@ llama3.1, llama3.2, qwen2.5, deepseek-r1-distill, gemma-3, granite, SauerkrautLM
 <b>&#x21e8;</b> best models for english and german:<br>
 granit3.2-8b (2b version also) - https://huggingface.co/ibm-research/granite-3.2-8b-instruct-GGUF<br>
 Chocolatine-2-14B (other versions also) - https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b11-GGUF<br>
-QwQ-LCoT- (7/14b) - https://huggingface.co/mradermacher/QwQ-LCoT-14B-Conversational-GGUF<br><br>
 ...
 # Important -> The Systemprompt (some examples):

 <b>&#x21e8;</b> best models for english and german:<br>
 granit3.2-8b (2b version also) - https://huggingface.co/ibm-research/granite-3.2-8b-instruct-GGUF<br>
 Chocolatine-2-14B (other versions also) - https://huggingface.co/mradermacher/Chocolatine-2-14B-Instruct-DPO-v2.0b11-GGUF<br>
+QwQ-LCoT- (7/14B) - https://huggingface.co/mradermacher/QwQ-LCoT-14B-Conversational-GGUF<br>
+gemma-3 (4/12/27B) - https://huggingface.co/bartowski/google_gemma-3-12b-it-GGUF<br><br>
 ...
 # Important -> The Systemprompt (some examples):