license	base_model	base_model_relation
cc-by-nc-4.0	jinaai/jina-embeddings-v4	quantized

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

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:

HuggingFace Repo	Task
jinaai/jina-embeddings-v4-text-retrieval-GGUF	Text retrieval
jinaai/jina-embeddings-v4-text-code-GGUF	Code retrieval
jinaai/jina-embeddings-v4-text-matching-GGUF	Sentence similarity

All models above provide F16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q3_K_M and dynamic quantizations such as IQ1_S, IQ2_XXS.

Limitations vs original v4 model

• They can not handle image input.
• They can not output multi-vector embeddings.
• You must add Query: or Passage: in front of the input. Check this table for the details.

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": [
      "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"
    ]
  }'

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",
      "Passage: 海滩上美丽的日落",
      "Passage: 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 "Query: jina is awesome" --embd-output-format json  2>/dev/null

Remarks

Consistency wrt. AutoModel.from_pretrained

To get fully consistent results as if you were using AutoModel.from_pretrained("jinaai/jina-embeddings-v4")..., you need to be very careful about the prefixes and manually add them to your GGUF model inputs. Here's a reference table:

Input Type	Task	prompt_name (Role)	Actual Input Processed by Model
Text	retrieval	query (default)	Query: {original_text}
Text	retrieval	passage	Passage: {original_text}
Text	text-matching	query (default)	Query: {original_text}
Text	text-matching	passage	Query: {original_text} ⚠️
Text	code	query (default)	Query: {original_text}
Text	code	passage	Passage: {original_text}
Image	Any task	N/A	<|im_start|>user\n<|vision_start|>\<|image_pad|>\<|vision_end|>Describe the image.\<|im_end|>

To some users, ⚠️ indicates a somewhat surprising behavior where prompt_name='passage' gets overridden to "Query: " when using text-matching in the original AutoModel.from_pretrained("jinaai/jina-embeddings-v4").... However, this is reasonable since text-matching is a sentence similarity task with no left/right roles—the inputs are symmetric.

Matryoshka embeddings

Note, v4 is trained with Matryoshka embeddings, and converting to GGUF doesn't break the Matryoshka feature. Let's say you get embeddings with shape NxD - you can simply use embeddings[:, :truncate_dim] to get smaller truncated embeddings. Note that not every dimension is trained though. For v4, you can set truncate_dim to any of these values: [128, 256, 512, 1024, 2048].

Quantizations

We use llama-quantize with imatrix to quantize models from float16. imatrix is generated by llama-imatrix -m jina-embeddings-v4-text-retrieval-F16.gguf -f calibration_data_v5_rc.txt -ngl 99 --no-ppl -o imatrix-retrieval-512.dat. calibration_data_v5_rc.txt can be found here and is recommended by Unsloth docs.