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

qherreros commited on Feb 16

Commit

44aa200

verified ·

1 Parent(s): 92ae623

Update README.md

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README.md CHANGED Viewed

@@ -42,9 +42,10 @@ For training details and evaluation results, see our [technical report](https://
 The following Python packages are required:
-- `transformers>=4.57.0`
 - `torch>=2.8.0`
 - `peft>=0.15.2`
 ### Optional / Recommended
 - **flash-attention**: Installing [flash-attention](https://github.com/Dao-AILab/flash-attention) is recommended for improved inference speed and efficiency, but not mandatory.
@@ -60,7 +61,12 @@ from vllm import LLM
 # Initialize model
 name = "jinaai/jina-embeddings-v5-text-small-clustering"
-model = LLM(model=name, task="embed", dtype="bfloat16")
 # Create text prompts
 document1 = "Overview of climate change impacts on coastal cities"

 The following Python packages are required:
+- `transformers>=5.1.0`
 - `torch>=2.8.0`
 - `peft>=0.15.2`
+- `vllm>=0.15.1`
 ### Optional / Recommended
 - **flash-attention**: Installing [flash-attention](https://github.com/Dao-AILab/flash-attention) is recommended for improved inference speed and efficiency, but not mandatory.
 # Initialize model
 name = "jinaai/jina-embeddings-v5-text-small-clustering"
+model = LLM(
+    model=name,
+    dtype="float16",
+    runner="pooling",
+    pooler_config=PoolerConfig(seq_pooling_type="LAST", normalize=True)
+)
 # Create text prompts
 document1 = "Overview of climate change impacts on coastal cities"