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] - 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 embeddings server: llama serve -hf kalle07/embedder_collection:Q4_K_M --embeddings
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf kalle07/embedder_collection:Q4_K_M --embeddings
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf kalle07/embedder_collection:Q4_K_M --embeddings
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 embeddings server: ./build/bin/llama-server -hf kalle07/embedder_collection:Q4_K_M --embeddings
- 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
Update README.md
Browse files
README.md
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@@ -81,9 +81,9 @@ but ~5000 characters is one page of a book (no matter ger/en). But if you calcul
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The example is english, for german you can add apox 50% more token/word (1000 words ~1800t)<br>
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<li>1200t (~1000 words ~5000 chracter) ~0.1GB, this is aprox one page with small font</li>
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<li>best to get in mind: 5000-6000 characters correspond to approximately one page and approximately 1200-1400 token.</li>
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<li>8000t (~6000 words) ~
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<li>16000t (~12000 words) ~
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<li>32000t (~24000 words) ~
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The example is english, for german you can add apox 50% more token/word (1000 words ~1800t)<br>
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<li>1200t (~1000 words ~5000 chracter) ~0.1GB, this is aprox one page with small font</li>
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<li>best to get in mind: 5000-6000 characters correspond to approximately one page and approximately 1200-1400 token.</li>
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<li>8000t (~6000 words) ~1.5GB VRAM usage</li>
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<li>16000t (~12000 words) ~3GB VRAM usage</li>
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<li>32000t (~24000 words) ~6GB VRAM usage</li>
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