The crispy rerank family from Mixedbread.
# Rewritten Mixedbread Reranker as classifier This repo is the Mixedbread reranker rewritten as Classifier that is as of March 2025 the most powerful reranker, e.g. for RAG. # FP8 Deployment on H100 ```yaml build_commands: [] environment_variables: {} external_package_dirs: [] model_metadata: example_model_input: input: 'ERROR: This redirects to the embedding endpoint. Use the /sync API to reach /sync/predict' model_name: BEI-mixedbread-ai-mxbai-rerank-base-v2-reranker-fp8-truss-example python_version: py39 requirements: [] resources: accelerator: L4 cpu: '1' memory: 10Gi use_gpu: true secrets: {} system_packages: [] trt_llm: build: base_model: encoder checkpoint_repository: repo: michaelfeil/mxbai-rerank-base-v2-seq revision: main source: HF max_num_tokens: 32768 max_seq_len: 1000001 num_builder_gpus: 4 quantization_type: fp8 ``` To push the deployment on Baseten.co ```bash truss push --publish ``` More info: https://github.com/basetenlabs/truss-examples/tree/main/11-embeddings-reranker-classification-tensorrt/BEI-mixedbread-ai-mxbai-rerank-base-v2-reranker-fp8 ## Usage as classifier For usage with Baseten.co or with github.com/michaelfeil/infinity, you should use the classification API. You need to manually create the following prompt template that is very specfic to this model. This template follows the reference implementation at https://github.com/mixedbread-ai/mxbai-rerank/tree/main. ```python def create_mxbai_v2_reranker_prompt_template(query: str, document: str, instruction: str = "") -> str: """ Create a carefully formatted chat template string (without tokenizer) for ranking relevance. Parameters: query (str): The search query. document (str): The document text to evaluate. instruction (str): Special instructions (e.g., "You are an expert for Mockingbirds.") Returns: str: The formatted chat template. """ instruction = f"instruction: {instruction}\n" if instruction else "" # fixed system prompt, keep as is. system_prompt = "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." assert not "\n" in system_prompt assert not "\n" in instruction[:-1] assert isinstance(query, str) assert isinstance(document, str) templated = ( # keep spacing, newlines as is. # template for mixedbread reranker v2 # https://huggingface.co/michaelfeil/mxbai-rerank-base-v2-seq/ f"<|endoftext|><|im_start|>system\n{system_prompt}\n" "<|im_end|>\n" "<|im_start|>user\n" f"{instruction}" f"query: {query} \n" f"document: {document} \n" "You are a search relevance expert who evaluates how well documents match search queries. " "For each query-document pair, carefully analyze the semantic relationship between them, then provide your binary relevance judgment (0 for not relevant, 1 for relevant).\n" "Relevance:<|im_end|>\n" "<|im_start|>assistant\n" ) return templated ``` # 🍞 mxbai-rerank-base-v2 This is the base model in our family of powerful reranker models. You can learn more about the models in our [blog post](https://www.mixedbread.ai/blog/mxbai-rerank-v2). We have two models: - [mxbai-rerank-base-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-base-v2) (🍞) - [mxbai-rerank-large-v2](https://huggingface.co/mixedbread-ai/mxbai-rerank-large-v2) **The technical report is coming soon!** ## 🌟 Features - state-of-the-art performance and strong efficiency - multilingual support (100+ languages, outstanding English and Chinese performance) - code support - long-context support ## ⚙️ Usage 1. Install mxbai-rerank ```bash pip install mxbai-rerank ``` 2. Inference ```python from mxbai_rerank import MxbaiRerankV2 model = MxbaiRerankV2("mixedbread-ai/mxbai-rerank-base-v2") query = "Who wrote 'To Kill a Mockingbird'?" documents = [ "'To Kill a Mockingbird' is a novel by Harper Lee published in 1960. It was immediately successful, winning the Pulitzer Prize, and has become a classic of modern American literature.", "The novel 'Moby-Dick' was written by Herman Melville and first published in 1851. It is considered a masterpiece of American literature and deals with complex themes of obsession, revenge, and the conflict between good and evil.", "Harper Lee, an American novelist widely known for her novel 'To Kill a Mockingbird', was born in 1926 in Monroeville, Alabama. She received the Pulitzer Prize for Fiction in 1961.", "Jane Austen was an English novelist known primarily for her six major novels, which interpret, critique and comment upon the British landed gentry at the end of the 18th century.", "The 'Harry Potter' series, which consists of seven fantasy novels written by British author J.K. Rowling, is among the most popular and critically acclaimed books of the modern era.", "'The Great Gatsby', a novel written by American author F. Scott Fitzgerald, was published in 1925. The story is set in the Jazz Age and follows the life of millionaire Jay Gatsby and his pursuit of Daisy Buchanan." ] # Lets get the scores results = model.rank(query, documents, return_documents=True, top_k=3) print(results) ``` ## Performance ### Benchmark Results | Model | BEIR Avg | Multilingual | Chinese | Code Search | Latency (s) | |-------|----------|----------|----------|--------------|-------------| | mxbai-rerank-large-v2 | 57.49 | 29.79 | 84.16 | 32.05 | 0.89 | | mxbai-rerank-base-v2 | 55.57 | 28.56 | 83.70 | 31.73 | 0.67 | | mxbai-rerank-large-v1 | 49.32 | 21.88 | 72.53 | 30.72 | 2.24 | *Latency measured on A100 GPU ## Training Details The models were trained using a three-step process: 1. **GRPO (Guided Reinforcement Prompt Optimization)** 2. **Contrastive Learning** 3. **Preference Learning** For more details, check our [technical blog post](https://mixedbread.com/blog/mxbai-rerank-v2). Paper following soon. ## 🎓 Citation ```bibtex @online{rerank2025mxbai, title={Every Byte Matters: Introducing mxbai-embed-xsmall-v1}, author={Sean Lee and Aamir Shakir and Julius Lipp and Rui Huang}, year={2025}, url={https://www.mixedbread.com/blog/mxbai-rerank-v2}, } ```