Instructions to use jinaai/jina-embeddings-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jinaai/jina-embeddings-v4 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jinaai/jina-embeddings-v4", trust_remote_code=True, device_map="auto") - ColPali
How to use jinaai/jina-embeddings-v4 with ColPali:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- sentence-transformers
How to use jinaai/jina-embeddings-v4 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jinaai/jina-embeddings-v4", trust_remote_code=True) 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] - Notebooks
- Google Colab
- Kaggle
The official jina embedding API is very slow
The official rate is at 2,000 RPM & 5,000,000 TPM, but I found that at around 100-300 RPM, each response time is very long, possibly reaching 10-20s
Thanks for the details — that’s helpful. A quick note: jina-embeddings-v4 is currently in a research/experimental mode on our side, so it isn’t provisioned with the same dedicated capacity as our production-tier models. Because of that, latency can be variable and may spike during load or contention, even if the documented RPM/TPM limits are higher.
To help us pinpoint what you’re seeing, could you clarify how you’re measuring “100–300 RPM”?
- Measurement window: is that per-second burst converted to RPM, a 1-minute rolling average, or an average over a longer run?
- Concurrency: how many requests are in-flight at once (e.g., 1, 10, 50)?
- Input shape: average input length (chars/tokens) and batch size per request