Instructions to use jinaai/jina-embeddings-v5-text-small-clustering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use jinaai/jina-embeddings-v5-text-small-clustering with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v5-text-small-clustering") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - llama-cpp-python
How to use jinaai/jina-embeddings-v5-text-small-clustering with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jinaai/jina-embeddings-v5-text-small-clustering", filename="v5-small-clustering-F16.gguf", )
llm.create_chat_completion( messages = "\"Today is a sunny day and I will get some ice cream.\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use jinaai/jina-embeddings-v5-text-small-clustering 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 jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M # Run inference directly in the terminal: llama cli -hf jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M # Run inference directly in the terminal: llama cli -hf jinaai/jina-embeddings-v5-text-small-clustering: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 jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf jinaai/jina-embeddings-v5-text-small-clustering: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 jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
Use Docker
docker model run hf.co/jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use jinaai/jina-embeddings-v5-text-small-clustering with Ollama:
ollama run hf.co/jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
- Unsloth Studio
How to use jinaai/jina-embeddings-v5-text-small-clustering 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 jinaai/jina-embeddings-v5-text-small-clustering 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 jinaai/jina-embeddings-v5-text-small-clustering to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jinaai/jina-embeddings-v5-text-small-clustering to start chatting
- Pi
How to use jinaai/jina-embeddings-v5-text-small-clustering with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use jinaai/jina-embeddings-v5-text-small-clustering with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use jinaai/jina-embeddings-v5-text-small-clustering with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use jinaai/jina-embeddings-v5-text-small-clustering with Docker Model Runner:
docker model run hf.co/jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
- Lemonade
How to use jinaai/jina-embeddings-v5-text-small-clustering with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jinaai/jina-embeddings-v5-text-small-clustering:Q4_K_M
Run and chat with the model
lemonade run user.jina-embeddings-v5-text-small-clustering-Q4_K_M
List all available models
lemonade list
Add `text-embeddings-inference` snippet in `README.md` (#2)
Browse files- Add `text-embeddings-inference` snippet in `README.md` (6db8e323f74bdfa2b7f7f12ab6c4112c30663413)
Co-authored-by: Alvaro Bartolome <alvarobartt@users.noreply.huggingface.co>
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<details>
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<summary> via <a href="https://github.com/ggml-org/llama.cpp">llama.cpp (GGUF)</a></summary>
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After installing <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> one can run llama-server to host the embedding model as OpenAI API compatible HTTP server with the respective model version:
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</details>
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<details>
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<summary>via <a href="https://github.com/huggingface/text-embeddings-inference">Text Embeddings Inference</a></summary>
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- Via Docker on CPU:
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```bash
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docker run -p 8080:80 \
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ghcr.io/huggingface/text-embeddings-inference:cpu-1.9 \
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--model-id jinaai/jina-embeddings-v5-text-small-clustering \
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--dtype float32 --pooling last-token
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```
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- Via Docker on NVIDIA GPU (Turing, Ampere, Ada Lovelace, Hopper or Blackwell):
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```bash
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docker run --gpus all --shm-size 1g -p 8080:80 \
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ghcr.io/huggingface/text-embeddings-inference:cuda-1.9 \
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--model-id jinaai/jina-embeddings-v5-text-small-clustering \
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--dtype float16 --pooling last-token
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```
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> Alternatively, you can also run with `cargo`, more information can be found in the [Text Embeddings Inference documentation](https://hf.co/docs/text-embeddings-inference).
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Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):
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```bash
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curl -X POST http://127.0.0.1:8080/v1/embeddings \
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-H "Content-Type: application/json" \
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-d '{
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"model": "jinaai/jina-embeddings-v5-text-small-clustering",
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"input": [
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"Query: Overview of climate change impacts on coastal cities",
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"Document: The impacts of climate change on coastal cities are significant...",
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]
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}'
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```
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Or rather via the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead, to prevent from manually formatting the inputs:
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```bash
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curl -X POST http://127.0.0.1:8080/embed \
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-H "Content-Type: application/json" \
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-d '{
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"inputs": "Overview of climate change impacts on coastal cities",
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"prompt_name": "query",
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}'
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```
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</details>
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<details>
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<summary> via <a href="https://github.com/ggml-org/llama.cpp">llama.cpp (GGUF)</a></summary>
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After installing <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> one can run llama-server to host the embedding model as OpenAI API compatible HTTP server with the respective model version:
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