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

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:885f00a9bbfe182a44a4000bbf9c5f5135f4bcd00fbf93b863f6624d9f929408
-size 1198182432

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c855d88e7f746af16030fd912a9d4fe78bc7fe68a1a897a9da71cf8db462c155
-size 216052064

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e6f51251cd4fbd51e2e26c4cfc23f9bd392e968d40bce30da7129404e99dea7a
-size 208015712

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1e9d140589d9d860748f915826933683e20ae96b59fa6d9c813e041442d5ba09
-size 264909152

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6e579b8147508f28542dce05a41c825785a7d3014cfd500c750cef5438e6ba58
-size 229445984

jinaai
/

jina-embeddings-v5-text-small-clustering

add pooling_type to gguf files

 version https://git-lfs.github.com/spec/v1
+oid sha256:683e78eeb6aae4461bf3c97e1b617337a218dbef37763b249a657286ec26776d
+size 1198182464

 version https://git-lfs.github.com/spec/v1
+oid sha256:592b04234ee2eaad2da9714bdee0cb29edba35bdb8696a26b435b84b3b491cdf
+size 216052096

 version https://git-lfs.github.com/spec/v1
+oid sha256:32699d9c5bcf0d3b52fcf54d87971cf0e74b1b04af9d82999884bc3b22c13ce8
+size 208015744

 version https://git-lfs.github.com/spec/v1
+oid sha256:3e3b5c8698fb193ba56ce224eb072578b467d2194ad09381a7170567632c584b
+size 264909184

 version https://git-lfs.github.com/spec/v1
+oid sha256:15aaa7545ad192c2d720f3bc71f4edcb977b75d09a9f4006c46715b648b7ee9f
+size 229446016

 version https://git-lfs.github.com/spec/v1
+oid sha256:d9269fd367e3befbdfb570ad42f63ff704edb078eeedfe809ff694dc737b8b00
+size 381566336

 version https://git-lfs.github.com/spec/v1
+oid sha256:51e8666160f757ba199d60f2fd4350c2f51f29421ee51946156be6212fec64eb
+size 367803776

 version https://git-lfs.github.com/spec/v1
+oid sha256:0c5054f34bab42d7d4e4fb7e256fb3b72aec7e44e0877c1de4af4d28fff3c5e6
+size 296238464

 version https://git-lfs.github.com/spec/v1
+oid sha256:bfe943ac44842cad2a3782a37d42cf060018132926d15f10d499207bc837e112
+size 347127168

 version https://git-lfs.github.com/spec/v1
+oid sha256:c475f69d601c47c5cbf31e9bf1ab6df1bcaba058769b05cee9a11059f7ba7a1c
+size 396705152

 version https://git-lfs.github.com/spec/v1
+oid sha256:c0b1b40c51da17dd4830039c8fc50490190c1546f07e07902500f346e70eb3a3
+size 444415360

 version https://git-lfs.github.com/spec/v1
+oid sha256:f3daae28e4e928503d7483a7e3d40822ec6d17c0e5f3a563b89b95bfa872f984
+size 436616576

 version https://git-lfs.github.com/spec/v1
+oid sha256:8c6c3f50da76ab51fe92f3954f0a684ec2a90f84ca6671ed8616201883cf3713
+size 495107456

 version https://git-lfs.github.com/spec/v1
+oid sha256:c314b003b0f056ed3686031d84bfbaad702b428b9c2b89fc19b924d159e79a2d
+size 639447424