jinaai / jina-embeddings-v4-text-code-GGUF

jina-embeddings-v4-gguf

A collection of GGUF and quantizations for jina-embeddings-v4 .

Overview

jina-embeddings-v4 is a cutting-edge universal embedding model for multimodal multilingual retrieval . It's based on qwen2.5-vl-3b-instruct with three LoRA adapters: retrieval (optimized for retrieval tasks), text-matching (optimized for sentence similarity tasks), and code (optimized for code retrieval tasks). It is also heavily trained for visual document retrieval and late-interaction style multi-vector output.

Text-Only Task-Specific Models

Here, we removed the visual components of qwen2.5-vl and merged all LoRA adapters back into the base language model. This results in three task-specific v4 models with 3.09B parameters, downsized from the original jina-embeddings-v4 3.75B parameters:

All models above provide F16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q3_K_M quantizations. More quantizations such as Unsloth-like dynamic quantizations are on the way.

Limitations

• They can not handle image input.
• They can not output multi-vector embeddings.
• When using retrieval and code models, you must add Query: or Passage: in front of the input. This ensure the query and retrieval targets are correctly embedded into the correct space.

Multimodal Task-Specific Models

TBA

Get Embeddings

First install llama.cpp .

Run llama-server to host the embedding model as OpenAI API compatible HTTP server. As an example for using text-matching with F16 , you can do:

llama-server -hf jinaai/jina-embeddings-v4-text-matching-GGUF:F16 --embedding --pooling mean -ub 8192

Remarks:

• --pooling mean is required as v4 is mean-pooling embeddings.
• setting --pooling none is not as same as the multi-vector embeddings of v4. The original v4 has a trained MLP on top of the last hidden states to output multi-vector embeddings, each has 128-dim. In GGUF, this MLP was chopped off.

Client:

curl -X POST "http://127.0.0.1:8080/v1/embeddings" \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      "A beautiful sunset over the beach",
      "Un beau coucher de soleil sur la plage",
      "海滩上美丽的日落",
      "浜辺に沈む美しい夕日"
    ]
  }'

Note: When using retrieval and code models, add Query: or Passage: in front of your input, like this:

curl -X POST "http://127.0.0.1:8080/v1/embeddings" \
  -H "Content-Type: application/json" \
  -d '{
    "input": [
      "Query: A beautiful sunset over the beach",
      "Query: Un beau coucher de soleil sur la plage",
      "Query: 海滩上美丽的日落",
      "Query: 浜辺に沈む美しい夕日"
    ]
  }'

You can also use llama-embedding for one-shot embedding:

llama-embedding -hf jinaai/jina-embeddings-v4-text-matching-GGUF:F16 --pooling mean -p "jina is awesome"  2>/dev/null