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

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

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

michael-guenther commited on Feb 18

Commit

cc04279

verified ·

1 Parent(s): 2b2b8ed

Update README.md

Browse files

Files changed (1) hide show

README.md +13 -3

README.md CHANGED Viewed

@@ -26,7 +26,7 @@ library_name: llama.cpp
 ### **jina-embeddings-v5-text-small-clustering**: Clustering-Targeted Embedding Distillation
-[Blog](https://picsum.photos/200/300) | [Technical Report](https://picsum.photos/200/300) | [Jina API](https://jina.ai/embeddings) | [EIS](https://www.elastic.co/docs/explore-analyze/elastic-inference/eis)
 ### Model Overview
@@ -48,7 +48,7 @@ Trained using a novel approach that combines distillation with task-specific con
 ### Training and Evaluation
-For training details and evaluation results, see our [technical report](https://picsum.photos/200/300).
 ### Usage
@@ -221,4 +221,14 @@ The model is licensed under CC BY-NC 4.0. For commercial use, please [contact us
 If you find `jina-embeddings-v5-text-small-clustering` useful in your research, please cite the following paper:
-[will be published soon]

 ### **jina-embeddings-v5-text-small-clustering**: Clustering-Targeted Embedding Distillation
+[Blog](https://picsum.photos/200/300) | [Technical Report](https://arxiv.org/abs/2602.15547) | [Jina API](https://jina.ai/embeddings) | [EIS](https://www.elastic.co/docs/explore-analyze/elastic-inference/eis)
 ### Model Overview
 ### Training and Evaluation
+For training details and evaluation results, see our [technical report](https://arxiv.org/abs/2602.15547).
 ### Usage
 If you find `jina-embeddings-v5-text-small-clustering` useful in your research, please cite the following paper:
+```
+@misc{akram2026jinaembeddingsv5texttasktargetedembeddingdistillation,
+      title={jina-embeddings-v5-text: Task-Targeted Embedding Distillation},
+      author={Mohammad Kalim Akram and Saba Sturua and Nastia Havriushenko and Quentin Herreros and Michael Günther and Maximilian Werk and Han Xiao},
+      year={2026},
+      eprint={2602.15547},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2602.15547},
+}
+```