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

hanxiao commited on Feb 19

Commit

c8e37e4

verified ·

1 Parent(s): 3f35865

Add architecture diagram and benchmark scatter plots to README

Browse files

Files changed (1) hide show

README.md +19 -10

README.md CHANGED Viewed

@@ -30,17 +30,16 @@ library_name: llama.cpp
 ### Model Overview
 `jina-embeddings-v5-text-small-clustering` is a compact, high-performance text embedding model designed for clustering.
 It is part of the **jina-embeddings-v5-text** model family, which also includes [jina-embeddings-v5-text-nano](https://huggingface.co/jinaai/jina-embeddings-v5-text-nano), a smaller model for more resource-constrained use cases.
 Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-small-clustering` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks.
-<p align="center">
-<img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5_architecture_1771470917.png" alt="jina-embeddings-v5-text Architecture" width="600px">
-</p>
 | Feature | Value |
 | --- | --- |
 | Parameters | 677M |
@@ -51,6 +50,20 @@ Trained using a novel approach that combines distillation with task-specific con
 | Pooling Strategy | Last-token pooling |
 | Base Model | jinaai/jina-embeddings-v5-text-small |
 ### Training and Evaluation
 For training details and evaluation results, see our [technical report](https://arxiv.org/abs/2602.15547).
@@ -136,7 +149,6 @@ document2_prompt = f"Document: {document2}"
 prompts = [document1_prompt, document2_prompt]
 outputs = model.encode(prompts, pooling_task="embed")
 embed_document1 = outputs[0].outputs.data
 embed_document2 = outputs[1].outputs.data
 ```
@@ -197,7 +209,6 @@ After installing <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> o
 llama-server -hf jinaai/jina-embeddings-v5-text-small-clustering:F16 --embedding --pooling last -ub 32768
 ```
 Client:
 ```
@@ -214,14 +225,12 @@ curl -X POST "http://127.0.0.1:8080/v1/embeddings" \
   }'
 ```
 </details>
 ### License
 The model is licensed under CC BY-NC 4.0. For commercial use, please [contact us](sales@jina.ai).
 ### Citation
 If you find `jina-embeddings-v5-text-small-clustering` useful in your research, please cite the following paper:

 ### Model Overview
+<p align="center">
+<img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5_architecture_1771470917.png" alt="jina-embeddings-v5-text Architecture" width="600px">
+</p>
 `jina-embeddings-v5-text-small-clustering` is a compact, high-performance text embedding model designed for clustering.
 It is part of the **jina-embeddings-v5-text** model family, which also includes [jina-embeddings-v5-text-nano](https://huggingface.co/jinaai/jina-embeddings-v5-text-nano), a smaller model for more resource-constrained use cases.
 Trained using a novel approach that combines distillation with task-specific contrastive losses, `jina-embeddings-v5-text-small-clustering` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks.
 | Feature | Value |
 | --- | --- |
 | Parameters | 677M |
 | Pooling Strategy | Last-token pooling |
 | Base Model | jinaai/jina-embeddings-v5-text-small |
+<p align="center">
+<img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5_mmteb-2.png" alt="MMTEB Multilingual Benchmark" width="500px">
+</p>
+<p align="center">
+<img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5_mteb_en-2.png" alt="MTEB English Benchmark" width="500px">
+</p>
+<p align="center">
+<img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5_retrieval-2.png" alt="Retrieval Benchmark Results" width="500px">
+</p>
 ### Training and Evaluation
 For training details and evaluation results, see our [technical report](https://arxiv.org/abs/2602.15547).
 prompts = [document1_prompt, document2_prompt]
 outputs = model.encode(prompts, pooling_task="embed")
 embed_document1 = outputs[0].outputs.data
 embed_document2 = outputs[1].outputs.data
 ```
 llama-server -hf jinaai/jina-embeddings-v5-text-small-clustering:F16 --embedding --pooling last -ub 32768
 ```
 Client:
 ```
   }'
 ```
 </details>
 ### License
 The model is licensed under CC BY-NC 4.0. For commercial use, please [contact us](sales@jina.ai).
 ### Citation
 If you find `jina-embeddings-v5-text-small-clustering` useful in your research, please cite the following paper: