mxbai-rerank-large-v2
mxbai-rerank-large-v2 is a 1.5B-parameter text ranking model from mixedbread-ai, distributed under Apache 2.0. It ranks relevance of text passages to queries and supports 40+ languages. The model is publicly available (not gated), has modest download activity (47K), and appears actively maintained as of April 2026. It is suitable for retrieval-augmented generation (RAG) and search relevance tasks.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | mixedbread-ai |
| Parameters | 1.5B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-ranking |
| Gated on HuggingFace | No |
| Downloads | 47.8k |
| Likes | 141 |
| Last updated | 2026-04-08 |
| Source | mixedbread-ai/mxbai-rerank-large-v2 |
What mxbai-rerank-large-v2 is
A transformer-based text ranking model built on Qwen2, quantized in safetensors format. Context length is not specified in the data. The model is designed to score text pairs for relevance ranking in information retrieval pipelines. Multi-lingual support across 40+ language codes (af, am, ar, as, az, be, bg, bn, br, bs, ca, cs, cy, da, de, el, en, eo, es, et, eu, fa, ff, fi, and others implied). Integration with Hugging Face Transformers and sentence-transformers ecosystem.
Run mxbai-rerank-large-v2 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="mixedbread-ai/mxbai-rerank-large-v2")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: ~6–12 GB GPU VRAM for fp32 inference on typical batch sizes; likely 3–6 GB in int8 or bfloat16. Exact requirements depend on batch size, sequence length, and serving framework. Context length is not specified, so maximum input size is unknown—verify empirically.
Model is based on Qwen2 and compatible with sentence-transformers. LoRA or QLoRA fine-tuning is plausible for domain-specific ranking tasks, but no official fine-tuning guidance or recipes are provided in the data. Requires manual setup and validation on your ranking dataset.
When to avoid it — and what to weigh
- If you need text generation, not ranking — This model is a ranker, not a generative LLM. It does not produce new text; it scores pairs. If you need to generate responses, pair with a separate LLM.
- If you require guaranteed sub-latency SLA — Ranking latency depends on batch size, hardware, and serving framework. No latency benchmarks provided in the data. Test in your environment before committing to SLAs.
- If you need a closed/proprietary model for regulated environments — The model is open-source (Apache 2.0) and code is publicly visible. If your compliance requires proprietary code audits or contractual support, consider a commercial alternative.
- If you need a model with documented fine-tuning benchmarks — No fine-tuning results, LoRA feasibility metrics, or transfer-learning benchmarks are provided. Custom adaptation may require manual validation.
License & commercial use
Apache License 2.0 (OSI-approved, permissive open-source license). Permits use, modification, and distribution under terms of the license.
Apache 2.0 explicitly permits commercial use, including incorporation in closed-source products, provided the license and copyright notice are included. No additional commercial license required. Attribution and preservation of license text in distributions recommended.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Limited |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Model is public and code is open-source. No dedicated security audit or adversarial robustness assessment mentioned. Standard transformer security practices apply: validate and sanitize inputs; be aware of potential information leakage if sensitive data is ranked; use in isolated environments if processing confidential documents. Multi-lingual support does not guarantee equitable model behavior across all languages.
Alternatives to consider
BAAI/bge-reranker-large
Established dense reranker with published benchmarks; stronger community adoption and clearer fine-tuning documentation.
Cohere Rerank API
Proprietary service with SLA, support, and optional commercial license; eliminates self-hosting complexity at cost of vendor lock-in.
Sentence-Transformers Cross-Encoder (custom trained)
Fine-tune your own cross-encoder on domain data using sentence-transformers library; offers maximum control and domain adaptation.
Ship mxbai-rerank-large-v2 with senior software developers
Integrate this open-source reranker into your RAG or search system to boost result relevance without licensing friction. Supports 40+ languages, runs on modest GPU hardware, and is Apache 2.0 licensed for commercial use. Get started with Hugging Face Transformers or a containerized inference service.
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mxbai-rerank-large-v2 FAQ
Can I use this model commercially?
What is the context length (max input size)?
How do I serve this in production?
Does this model support fine-tuning?
Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If mxbai-rerank-large-v2 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Improve Your Retrieval Pipeline with mxbai-rerank-large-v2
Integrate this open-source reranker into your RAG or search system to boost result relevance without licensing friction. Supports 40+ languages, runs on modest GPU hardware, and is Apache 2.0 licensed for commercial use. Get started with Hugging Face Transformers or a containerized inference service.