Instructions to use jinaai/jina-embeddings-v5-text-small-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jinaai/jina-embeddings-v5-text-small-classification with sentence-transformers:

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("jinaai/jina-embeddings-v5-text-small-classification")

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-classification 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-classification",
	filename="v5-small-classification-F16.gguf",
)

llm.create_chat_completion(
	messages = "\"I like you. I love you\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use jinaai/jina-embeddings-v5-text-small-classification 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-classification:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jinaai/jina-embeddings-v5-text-small-classification: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-classification:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jinaai/jina-embeddings-v5-text-small-classification: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-classification:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jinaai/jina-embeddings-v5-text-small-classification: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-classification:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jinaai/jina-embeddings-v5-text-small-classification:Q4_K_M

Use Docker

docker model run hf.co/jinaai/jina-embeddings-v5-text-small-classification:Q4_K_M

LM Studio
Jan
Ollama
How to use jinaai/jina-embeddings-v5-text-small-classification with Ollama:
```
ollama run hf.co/jinaai/jina-embeddings-v5-text-small-classification:Q4_K_M
```

Unsloth Studio

How to use jinaai/jina-embeddings-v5-text-small-classification 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-classification 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-classification 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-classification to start chatting

How to use jinaai/jina-embeddings-v5-text-small-classification 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-classification: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-classification:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jinaai/jina-embeddings-v5-text-small-classification 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-classification: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-classification:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use jinaai/jina-embeddings-v5-text-small-classification 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-classification: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-classification: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-classification with Docker Model Runner:
```
docker model run hf.co/jinaai/jina-embeddings-v5-text-small-classification:Q4_K_M
```

Lemonade

How to use jinaai/jina-embeddings-v5-text-small-classification with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jinaai/jina-embeddings-v5-text-small-classification:Q4_K_M

Run and chat with the model

lemonade run user.jina-embeddings-v5-text-small-classification-Q4_K_M

List all available models

lemonade list

jina-embeddings-v5-text-small-classification / README.md

qherreros

Create README.md

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	---
	base_model:
	- Qwen/Qwen3-0.6B
	license: cc
	---
	<br><br>

	<p align="center">
	<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
	</p>

	### jina-embeddings-v5-text-small-classification: Classification-Targeted Embedding Distillation

	[Blog](https://picsum.photos/200/300) \| [Technical Report](https://picsum.photos/200/300) \| [API](https://jina.ai/embeddings)

	### Model Overview

	`jina-embeddings-v5-text-small-classification` is a compact, high-performance text embedding model designed for classification.

	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-classification` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks.

	\| Feature \| Value \|
	\| --- \| --- \|
	\| Parameters \| 677M \|
	\| Supported Tasks \| `classification`\|
	\| Max Sequence Length \| 32768 \|
	\| Embedding Dimension \| 1024 \|
	\| Matryoshka Dimensions \| 32, 64, 128, 256, 512, 768, 1024 \|
	\| Pooling Strategy \| Last-token pooling \|
	\| Base Model \| Qwen/Qwen3-0.6B-Base \|

	### Training and Evaluation

	For training details and evaluation results, see our [technical report](https://picsum.photos/200/300).

	### Usage

	<details>
	<summary>Requirements</a></summary>

	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.
	- sentence-transformers: If you want to use the model via the `sentence-transformers` interface, install this package as well.

	</details>

	<details>
	<summary>via <a href="https://github.com/vllm-project/vllm">vLLM</a></summary>

	```python
	from vllm import LLM

	# Initialize model
	name = "jinaai/jina-embeddings-v5-text-small-classification"
	model = LLM(model=name, task="embed", dtype="bfloat16")

	# Create text prompts
	document1 = "Overview of climate change impacts on coastal cities"
	document1_prompt = f"Document: {document1}"

	document2 = "The impacts of climate change on large cities"
	document2_prompt = f"Document: {document2}"

	# Encode all prompts
	prompts = [document1_prompt, document2_prompt]
	outputs = model.encode(prompts, pooling_task="embed")


	embed_document1 = outputs[0].outputs.data
	embed_document2 = outputs[1].outputs.data
	```

	</details>

	### License

	The model is licensed under CC BY-NC 4.0. For commercial use, please [contact us](link).


	### Citation

	If you find `jina-embeddings-v5-text-small-classification` useful in your research, please cite the following paper:

	[TODO]