Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+onnx/model.onnx_data filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,321 @@
+---
+pipeline_tag: feature-extraction
+tags:
+- gguf
+- embedding
+- qwen3
+- llama-cpp
+- jina-embeddings-v5
+- feature-extraction
+- mteb
+- vllm
+- sentence-transformers
+language:
+- multilingual
+base_model:
+- jinaai/jina-embeddings-v5-text-small-clustering
+base_model_relation: quantized
+inference: false
+license: cc-by-nc-4.0
+library_name: transformers.js
+---
+
+
+
+# jina-embeddings-v5-text-small-clustering (ONNX)
+
+
+This is an ONNX version of [jinaai/jina-embeddings-v5-text-small-clustering](https://huggingface.co/jinaai/jina-embeddings-v5-text-small-clustering). It was automatically converted and uploaded using [this Hugging Face Space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
+
+
+## Usage with Transformers.js
+
+
+See the pipeline documentation for `feature-extraction`: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.FeatureExtractionPipeline
+
+
+---
+
+
+<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-clustering**: Clustering-Targeted Embedding Distillation
+
+[Elastic Inference Service](https://www.elastic.co/docs/explore-analyze/elastic-inference/eis) | [ArXiv](https://arxiv.org/abs/2602.15547) | [Release Note](https://jina.ai/news/jina-embeddings-v5-text-distilling-4b-quality-into-sub-1b-multilingual-embeddings) | [Blog](https://www.elastic.co/search-labs/blog/jina-embeddings-v5-text)
+
+### Model Overview
+
+<p align="center">
+<img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5_architecture_1771470917.png" alt="jina-embeddings-v5-text Architecture" width="600px">
+</p>
+`jina-embeddings-v5-text-small-clustering` is a compact, high-performance text embedding model designed for clustering.
+
+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-clustering` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks.
+| Feature | Value |
+| --- | --- |
+| Parameters | 677M |
+| Supported Tasks | `clustering`|
+| Max Sequence Length | 32768 |
+| Embedding Dimension | 1024 |
+| Matryoshka Dimensions | 32, 64, 128, 256, 512, 768, 1024 |
+| Pooling Strategy | Last-token pooling |
+| Base Model | jinaai/jina-embeddings-v5-text-small |
+
+![v5_benchmarks_combined](https://cdn-uploads.huggingface.co/production/uploads/6476ff2699a5ce743ccea3fc/7WjMQChM6XAOI9LhREChg.png)
+
+
+### Training and Evaluation
+
+For training details and evaluation results, see our [technical report](https://arxiv.org/abs/2602.15547).
+
+### Usage
+
+<details>
+  <summary>Requirements</a></summary>
+  
+The following Python packages are required:
+
+- `transformers>=5.1.0`
+- `torch>=2.8.0`
+- `peft>=0.15.2`
+- `vllm>=0.15.1`
+  
+### 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 open>
+  <summary>via <a href="https://www.elastic.co/docs/explore-analyze/elastic-inference/eis">Elastic Inference Service</a></summary>
+
+The fastest way to use v5-text in production. Elastic Inference Service (EIS) provides managed embedding inference with built-in scaling, so you can generate embeddings directly within your Elastic deployment.
+
+```bash
+PUT _inference/text_embedding/jina-v5
+{
+  "service": "elastic",
+  "service_settings": {
+    "model_id": "jina-embeddings-v5-text-small"
+  }
+}
+```
+
+See the [Elastic Inference Service documentation](https://www.elastic.co/docs/explore-analyze/elastic-inference/eis) for setup details.
+
+</details>
+
+
+<details>
+  <summary>via <a href="https://sbert.net/">sentence-transformers</a></summary>
+
+```python
+from sentence_transformers import SentenceTransformer
+import torch
+
+model = SentenceTransformer(
+    "jinaai/jina-embeddings-v5-text-small-clustering",
+    model_kwargs={"dtype": torch.bfloat16},  # Recommended for GPUs
+    config_kwargs={"_attn_implementation": "flash_attention_2"},  # Recommended but optional
+)
+# Optional: set truncate_dim in encode() to control embedding size
+
+texts = [
+    "We propose a novel neural network architecture for image segmentation.",
+    "This paper analyzes the effects of monetary policy on inflation.",
+    "Our method achieves state-of-the-art results on object detection benchmarks.",
+    "We study the relationship between interest rates and housing prices.",
+    "A new attention mechanism is introduced for visual recognition tasks.",
+]
+
+# Encode texts
+embeddings = model.encode(texts)
+print(embeddings.shape)
+# (5, 1024)
+
+similarity = model.similarity(embeddings, embeddings)
+print(similarity)
+# tensor([[1.0000, 0.2983, 0.8631, 0.3098, 0.9106],
+#         [0.2983, 1.0000, 0.3257, 0.8041, 0.3201],
+#         [0.8631, 0.3257, 1.0000, 0.3263, 0.9007],
+#         [0.3098, 0.8041, 0.3263, 1.0000, 0.3122],
+#         [0.9106, 0.3201, 0.9007, 0.3122, 1.0000]])
+```
+</details>
+
+<details>
+  <summary>via <a href="https://github.com/vllm-project/vllm">vLLM</a></summary>
+
+```python
+from vllm import LLM#
+from vllm.config.pooler import PoolerConfig
+
+# Initialize model
+name = "jinaai/jina-embeddings-v5-text-small-clustering"
+model = LLM(
+    model=name,
+    dtype="float16",
+    runner="pooling",
+    pooler_config=PoolerConfig(seq_pooling_type="LAST", normalize=True)
+)
+
+# 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>
+
+<details>
+  <summary>via <a href="https://github.com/huggingface/text-embeddings-inference">Text Embeddings Inference</a></summary>
+
+- Via Docker on CPU:
+  ```bash
+  docker run -p 8080:80 \
+    ghcr.io/huggingface/text-embeddings-inference:cpu-1.9 \
+    --model-id jinaai/jina-embeddings-v5-text-small-clustering \
+    --dtype float32 --pooling last-token
+  ```
+- Via Docker on NVIDIA GPU (Turing, Ampere, Ada Lovelace, Hopper or Blackwell):
+  ```bash
+  docker run --gpus all --shm-size 1g -p 8080:80 \
+    ghcr.io/huggingface/text-embeddings-inference:cuda-1.9 \
+    --model-id jinaai/jina-embeddings-v5-text-small-clustering \
+    --dtype float16 --pooling last-token
+  ```
+
+> Alternatively, you can also run with `cargo`, more information can be found in the [Text Embeddings Inference documentation](https://hf.co/docs/text-embeddings-inference).
+
+Send a request to `/v1/embeddings` to generate embeddings via the [OpenAI Embeddings API](https://platform.openai.com/docs/api-reference/embeddings/create):
+
+```bash
+curl -X POST http://127.0.0.1:8080/v1/embeddings \
+  -H "Content-Type: application/json" \
+  -d '{
+    "model": "jinaai/jina-embeddings-v5-text-small-clustering",
+    "input": [
+      "Document: The impacts of climate change on coastal cities are significant...",
+    ]
+  }'
+```
+
+Or rather via the [Text Embeddings Inference API specification](https://huggingface.github.io/text-embeddings-inference/) instead, to prevent from manually formatting the inputs:
+
+```bash
+curl -X POST http://127.0.0.1:8080/embed \
+  -H "Content-Type: application/json" \
+  -d '{
+    "inputs": "Overview of climate change impacts on coastal cities",
+    "prompt_name": "document",
+  }'
+```
+
+</details>
+
+<details>
+  <summary> via <a href="https://github.com/ggml-org/llama.cpp">llama.cpp (GGUF)</a></summary>
+After installing <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> one can run llama-server to host the embedding model as OpenAI API compatible HTTP server with the respective model version:
+
+```sh
+llama-server -hf jinaai/jina-embeddings-v5-text-small-clustering:F16 --embedding --pooling last -ub 32768
+```
+
+Client:
+
+```
+curl -X POST "http://127.0.0.1:8080/v1/embeddings" \
+  -H "Content-Type: application/json" \
+  -d '{
+    "input": [
+      "Document: A beautiful sunset over the beach",
+      "Document: Un beau coucher de soleil sur la plage",
+      "Document: 海滩上美丽的日落",
+      "Document: 浜辺に沈む美しい夕日",
+      "Document: Golden sunlight melts into the horizon, painting waves in warm amber and rose, while the sky whispers goodnight to the quiet, endless sea."
+    ]
+  }'
+```
+
+</details>
+
+<details>
+  <summary> via <a href="https://huggingface.co/docs/optimum/index">Optimum (ONNX)</a></summary>
+
+You can run the ONNX-optimized version of the model locally using Hugging Face's `optimum` library. Make sure you have the required dependencies installed (e.g., `pip install optimum[onnxruntime] transformers torch`):
+
+```python
+from optimum.onnxruntime import ORTModelForFeatureExtraction
+from transformers import AutoTokenizer
+import torch
+
+model_id = "jinaai/jina-embeddings-v5-text-small-clustering"
+
+# 1. Load tokenizer and ONNX model
+# We specify the subfolder 'onnx' where the weights are located
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = ORTModelForFeatureExtraction.from_pretrained(
+    model_id,
+    subfolder="onnx",
+    file_name="model.onnx",
+    provider="CPUExecutionProvider",  # Or "CUDAExecutionProvider" for GPU
+    trust_remote_code=True,
+)
+
+# 2. Prepare input
+texts = ["Document: How do I use Jina ONNX models?", "Document: Information about semantic matching."]
+inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
+
+
+# 4. Inference
+with torch.no_grad():
+    outputs = model(**inputs)
+
+# 5. Pooling (Crucial for Jina-v5)
+# Jina-v5 uses LAST-TOKEN pooling.
+# We take the hidden state of the last non-padding token.
+last_hidden_state = outputs.last_hidden_state
+# Find the indices of the last token (usually the end of the sequence)
+sequence_lengths = inputs.attention_mask.sum(dim=1) - 1
+embeddings = last_hidden_state[torch.arange(last_hidden_state.size(0)), sequence_lengths]
+
+print('embeddings shape:', embeddings.shape)
+print('embeddings:', embeddings)
+```
+
+</details>
+
+### License
+
+The model is licensed under CC BY-NC 4.0. For commercial use, please [contact us](sales@jina.ai).
+
+### Citation
+
+If you find `jina-embeddings-v5-text-small-clustering` useful in your research, please cite the following paper:
+
+```
+@misc{akram2026jinaembeddingsv5texttasktargetedembeddingdistillation,
+      title={jina-embeddings-v5-text: Task-Targeted Embedding Distillation}, 
+      author={Mohammad Kalim Akram and Saba Sturua and Nastia Havriushenko and Quentin Herreros and Michael Günther and Maximilian Werk and Han Xiao},
+      year={2026},
+      eprint={2602.15547},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2602.15547}, 
+}
+```

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,89 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

config.json

@@ -0,0 +1,70 @@
+{
+  "architectures": [
+    "Qwen3Model"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "float32",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 3500000,
+    "rope_type": "default"
+  },
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": null,
+  "task_names": [
+    "retrieval",
+    "text-matching",
+    "clustering",
+    "classification"
+  ],
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.6",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

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special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
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+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

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+{
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+  "tokenizer_class": "Qwen2Tokenizer",
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+}