{ "hf_id": "jinaai/jina-embeddings-v5-text-small", "meta": { "title": "Jina Embeddings v5 Text Small", "slug": "jina-embeddings-v5-text-small", "provider": "Jina AI", "description": "Jina AI's fifth-gen multilingual text embedding model (677M, Qwen3-0.6B-Base) with task-specific LoRA adapters for retrieval, text-matching, classification, and clustering.", "date_added": "2026-05-09", "date_updated": "2026-05-09", "tasks": [ "embedding" ], "performance_headline": "71.7 MTEB English v2 / 67.7 MMTEB at <1B params, 119+ languages, 32K context", "related_recipes": [ "jinaai/jina-reranker-m0" ] }, "recommended_command": { "hardware": "h200", "strategy": "single_node_tp", "variant": "default", "node_count": 1, "deploy_type": "single_node", "env": {}, "docker_image": "vllm/vllm-openai:latest", "command": "vllm serve jinaai/jina-embeddings-v5-text-small-retrieval \\\n --trust-remote-code \\\n --runner pooling \\\n --tensor-parallel-size 1", "argv": [ "vllm", "serve", "jinaai/jina-embeddings-v5-text-small-retrieval", "--trust-remote-code", "--runner", "pooling", "--tensor-parallel-size", "1" ], "docker_command": "docker run --gpus all \\\n --privileged --ipc=host -p 8000:8000 \\\n -v ~/.cache/huggingface:/root/.cache/huggingface \\\n vllm/vllm-openai:latest jinaai/jina-embeddings-v5-text-small-retrieval \\\n --trust-remote-code \\\n --runner pooling \\\n --tensor-parallel-size 1", "docker_argv": [ "docker", "run", "--gpus", "all", "--privileged", "--ipc=host", "-p", "8000:8000", "-v", "~/.cache/huggingface:/root/.cache/huggingface", "vllm/vllm-openai:latest", "jinaai/jina-embeddings-v5-text-small-retrieval", "--trust-remote-code", "--runner", "pooling", "--tensor-parallel-size", "1" ], "strategy_spec": { "name": "single_node_tp", "deploy_type": "single_node", "display_name": "Tensor Parallel", "short_label": "TP", "orientation": "latency", "description": "Single-node tensor parallel. Splits the model across all local GPUs. TP size is set to the GPU count at deploy time. The simplest multi-GPU strategy — works for all model architectures.\n", "hardware_match": { "min_gpus": 1, "max_gpus": 8, "multi_node": false }, "vllm_args": [], "parallel_flag": "--tensor-parallel-size" }, "hardware_profile": { "brand": "NVIDIA", "generation": "hopper", "compute_arch": "sm90", "display_name": "H200", "description": "NVIDIA H200 SXM 141 GB HBM3e · 8-GPU HGX node", "gpu_count": 8, "vram_gb": 1128, "multi_node": false }, "alternatives": { "kv_store_centralized_mooncake": "/jinaai/jina-embeddings-v5-text-small/strategies/kv_store_centralized_mooncake.json", "kv_store_distributed_mooncake": "/jinaai/jina-embeddings-v5-text-small/strategies/kv_store_distributed_mooncake.json" }, "by_hardware": { "h200": "/jinaai/jina-embeddings-v5-text-small/hw/h200.json", "h100": "/jinaai/jina-embeddings-v5-text-small/hw/h100.json", "b200": "/jinaai/jina-embeddings-v5-text-small/hw/b200.json", "gb200": "/jinaai/jina-embeddings-v5-text-small/hw/gb200.json", "b300": "/jinaai/jina-embeddings-v5-text-small/hw/b300.json", "gb300": "/jinaai/jina-embeddings-v5-text-small/hw/gb300.json", "mi300x": "/jinaai/jina-embeddings-v5-text-small/hw/mi300x.json", "mi325x": "/jinaai/jina-embeddings-v5-text-small/hw/mi325x.json", "mi350x": "/jinaai/jina-embeddings-v5-text-small/hw/mi350x.json", "mi355x": "/jinaai/jina-embeddings-v5-text-small/hw/mi355x.json" } }, "model": { "model_id": "jinaai/jina-embeddings-v5-text-small", "min_vllm_version": "0.20.0", "architecture": "dense", "parameter_count": "0.7B", "active_parameters": "0.7B", "context_length": 32768, "base_args": [ "--trust-remote-code", "--runner", "pooling" ], "base_env": {}, "install": { "pip": { "command": "uv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto" }, "docker": { "command": "docker pull vllm/vllm-openai:latest" } } }, "features": {}, "opt_in_features": [], "variants": { "default": { "model_id": "jinaai/jina-embeddings-v5-text-small-retrieval", "label": "retrieval", "precision": "bf16", "vram_minimum_gb": 2, "description": "Retrieval task: query/document encoding for RAG and search. Adapter pre-merged into the base weights.", "json": "/jinaai/jina-embeddings-v5-text-small-retrieval.json" }, "text_matching": { "model_id": "jinaai/jina-embeddings-v5-text-small-text-matching", "label": "text-matching", "precision": "bf16", "vram_minimum_gb": 2, "description": "Text-matching task: semantic similarity, dedup, paraphrase detection. Adapter pre-merged into the base weights.", "json": "/jinaai/jina-embeddings-v5-text-small-text-matching.json" }, "classification": { "model_id": "jinaai/jina-embeddings-v5-text-small-classification", "label": "classification", "precision": "bf16", "vram_minimum_gb": 2, "description": "Classification task: linear probing, intent detection. Adapter pre-merged into the base weights.", "json": "/jinaai/jina-embeddings-v5-text-small-classification.json" }, "clustering": { "model_id": "jinaai/jina-embeddings-v5-text-small-clustering", "label": "clustering", "precision": "bf16", "vram_minimum_gb": 2, "description": "Clustering task: k-means, topic discovery. Adapter pre-merged into the base weights.", "json": "/jinaai/jina-embeddings-v5-text-small-clustering.json" } }, "hardware_overrides": {}, "guide": "## Overview\n\n[`jina-embeddings-v5-text-small`](https://huggingface.co/jinaai/jina-embeddings-v5-text-small)\nis the fifth generation of Jina AI's multilingual text embedding family, released\nFebruary 18, 2026. It scores **71.7 on MTEB English v2** and **67.7 on MMTEB** with\nonly 677M parameters — the highest among multilingual embedding models under 1B —\nand supports **119+ languages with up to 32K-token context**. Built on\n`Qwen3-0.6B-Base` and trained by distilling `Qwen3-Embedding-4B` plus task-specific\ncontrastive losses, it produces 1024-dim embeddings that stay robust under\ntruncation (Matryoshka dims: 32–1024) and binary quantization.\n\nvLLM support landed in [PR #39575](https://github.com/vllm-project/vllm/pull/39575)\nvia the `JinaEmbeddingsV5Model` architecture and the `--runner pooling` API.\n\n### Task-specific adapters\n\nv5 ships four LoRA adapters — one per supported task. For each task, Jina AI\npublishes a sibling repo with that adapter **pre-merged into the base weights**;\nthese are the simplest path for vLLM and what this recipe serves. Pick a task\nabove; the recipe swaps the model id accordingly:\n\n| Task | Pre-merged repo |\n|------|----------------|\n| Retrieval | `jinaai/jina-embeddings-v5-text-small-retrieval` |\n| Text-matching | `jinaai/jina-embeddings-v5-text-small-text-matching` |\n| Classification | `jinaai/jina-embeddings-v5-text-small-classification` |\n| Clustering | `jinaai/jina-embeddings-v5-text-small-clustering` |\n\n## Prerequisites\n\n- **Hardware**: any single GPU with ≥ 2 GB VRAM (T4 / L4 / A10 / A100 / H100 /\n H200 / B200 / MI300X all fine — bf16 weights are ~1.4 GB).\n- **vLLM**: requires a build that includes PR #39575. Use the nightly wheel\n until the next stable release ships:\n\n ```bash\n uv pip install -U vllm --pre \\\n --extra-index-url https://wheels.vllm.ai/nightly \\\n --index-strategy unsafe-best-match\n ```\n\n## Launch command\n\nUse the launch command above (it points at the chosen task's pre-merged repo).\nThe full form looks like:\n\n```bash\nvllm serve jinaai/jina-embeddings-v5-text-small-retrieval \\\n --trust-remote-code \\\n --runner pooling \\\n --host 0.0.0.0 --port 8000\n```\n\nSwap the model id for `-text-matching`, `-classification`, or `-clustering` to\nserve the corresponding task — or just toggle the **Variant** pill above.\n\n### Alternative: base repo + `--hf-overrides`\n\nIf you'd rather download a single checkpoint and switch tasks at startup, serve\nthe base `jinaai/jina-embeddings-v5-text-small` repo and pass the task via\n`--hf-overrides`:\n\n```bash\nvllm serve jinaai/jina-embeddings-v5-text-small \\\n --trust-remote-code \\\n --runner pooling \\\n --hf-overrides '{\"jina_task\": \"retrieval\"}'\n```\n\nAllowed values: `retrieval`, `text-matching`, `classification`, `clustering`.\nThis loads the base weights and merges the requested adapter at startup.\n\n## Client usage\n\n### Embeddings (`/v1/embeddings`)\n\n```bash\ncurl -s http://localhost:8000/v1/embeddings \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"jinaai/jina-embeddings-v5-text-small-retrieval\",\n \"input\": [\"Query: What is climate change?\"]\n }' | python3 -m json.tool\n```\n\n### Python (OpenAI SDK)\n\n```python\nfrom openai import OpenAI\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\")\n\nresp = client.embeddings.create(\n model=\"jinaai/jina-embeddings-v5-text-small-retrieval\",\n input=[\n \"Climate change has led to rising sea levels.\",\n \"Overview of climate change impacts on coastal cities.\",\n ],\n)\nfor d in resp.data:\n print(d.index, len(d.embedding))\n```\n\n### Multilingual text-matching example\n\nSwitch the **Variant** pill to **text-matching** (or serve the\n`-text-matching` repo) and embed semantically equivalent strings across\nlanguages:\n\n```python\ntexts = [\n \"غروب جميل على الشاطئ\",\n \"海滩上美丽的日落\",\n \"A beautiful sunset over the beach\",\n \"Un beau coucher de soleil sur la plage\",\n \"浜辺に沈む美しい夕日\",\n]\nresp = client.embeddings.create(\n model=\"jinaai/jina-embeddings-v5-text-small-text-matching\",\n input=texts,\n)\n```\n\n## Configuration tips\n\n- **Pick the task that matches your workload** — retrieval prompts (`query`\n vs. `document`) are baked into the retrieval adapter, so the wrong task\n measurably degrades recall.\n- **Matryoshka truncation**: embeddings stay useful when truncated to 32 / 64\n / 128 / 256 / 512 / 768 dims — keep the prefix and renormalize.\n- **Throughput**: with TP=1 on a single small GPU, the bottleneck is usually\n tokenization — batch your inputs (`input: [...]` with up to a few hundred\n short docs per request).\n- **bf16 vs fp16**: README recommends bf16 on modern GPUs; PR #39575's test\n used fp16. Either dtype works; bf16 is more numerically stable on Hopper /\n Blackwell / MI300X+.\n\n## References\n\n- [Model card](https://huggingface.co/jinaai/jina-embeddings-v5-text-small)\n- [Release blog](https://jina.ai/news/jina-embeddings-v5-text-distilling-4b-quality-into-sub-1b-multilingual-embeddings)\n- [Technical report (arXiv:2602.15547)](https://arxiv.org/abs/2602.15547)\n- [vLLM PR #39575 — Add Jina Embeddings v5 model support](https://github.com/vllm-project/vllm/pull/39575)\n- [Pre-merged retrieval repo](https://huggingface.co/jinaai/jina-embeddings-v5-text-small-retrieval)\n- [Pre-merged text-matching repo](https://huggingface.co/jinaai/jina-embeddings-v5-text-small-text-matching)\n- [Pre-merged classification repo](https://huggingface.co/jinaai/jina-embeddings-v5-text-small-classification)\n- [Pre-merged clustering repo](https://huggingface.co/jinaai/jina-embeddings-v5-text-small-clustering)\n", "source": { "source": "vllm", "repo": "vllm-project/vllm", "ref": "main", "sha": "389ffebe555705c85337025d5df108d0384d365e", "path": "recipes/models/jinaai/jina-embeddings-v5-text-small.yaml", "edit_url": "https://github.com/vllm-project/vllm/edit/main/recipes/models/jinaai/jina-embeddings-v5-text-small.yaml", "overlays": [] } }