| --- |
| base_model: |
| - meta-llama/Llama-3.1-8B |
| language: |
| - en |
| license: cc-by-nc-4.0 |
| pipeline_tag: feature-extraction |
| library_name: transformers |
| tags: |
| - sentence-transformers |
| --- |
| |
| ## Model Summary |
| ReasonIR-8B is the first retriever specifically trained for general reasoning tasks, achieving the state-of-the-art retrieval performance |
| on BRIGHT (reasoning-intensive retrieval). |
| When employed for retrieval-augmented generation (RAG), ReasonIR-8B also brings substantial gains on MMLU and GPQA. |
|
|
| - Paper: https://arxiv.org/abs/2504.20595 |
| - Repository: https://github.com/facebookresearch/ReasonIR |
| - Data: https://huggingface.co/datasets/reasonir/reasonir-data |
|
|
| ## Usage |
| Make sure to install `transformers>=4.47.0` first! |
|
|
| ### Transformers |
|
|
| ```python |
| from transformers import AutoModel |
| |
| model = AutoModel.from_pretrained("reasonir/ReasonIR-8B", torch_dtype="auto", trust_remote_code=True) |
| model = model.to("cuda") |
| model.eval() |
| |
| query = "The quick brown fox jumps over the lazy dog." |
| document = "The quick brown fox jumps over the lazy dog." |
| query_instruction = "" |
| doc_instruction = "" |
| |
| query_emb = model.encode(query, instruction=query_instruction) |
| doc_emb = model.encode(document, instruction=doc_instruction) |
| |
| sim = query_emb @ doc_emb.T |
| ``` |
|
|
| When using `AutoModel`, it is important to: |
|
|
| 1. Include `trust_remote_code=True` to make sure our custom bidirectional encoding architecture is used. |
| 2. Use `torch_dtype="auto"` so that `bf16` is activated (by default torch will use `fp32`). |
|
|
| ### Sentence Transformers |
|
|
| In addition to Transformers, you can also use this model with Sentence Transformers |
|
|
| ```python |
| # pip install sentence-transformers |
| from sentence_transformers import SentenceTransformer |
| |
| model_kwargs = {"torch_dtype": "auto"} |
| model = SentenceTransformer("reasonir/ReasonIR-8B", trust_remote_code=True, model_kwargs=model_kwargs) |
| |
| query = "The quick brown fox jumps over the lazy dog." |
| document = "The quick brown fox jumps over the lazy dog." |
| query_instruction = "" |
| doc_instruction = "" |
| |
| query_emb = model.encode(query, prompt=query_instruction) |
| doc_emb = model.encode(document, prompt=doc_instruction) |
| |
| sim = model.similarity(query_emb, doc_emb) |
| ``` |
|
|
| It is important to also include `trust_remote_code=True` and `torch_dtype="auto"` as discussed earlier. |
|
|
| > [!NOTE] |
| > There are some very slight floating point discrepancies when using the model via SentenceTransformer caused by how the models are cast to the `bfloat16` dtype, though it should not affect the results in general. |
|
|
| We thank [@tomaarsen](https://huggingface.co/tomaarsen) for improving the SentenceTransformer integration and analyzing the cause of the floating point discrepancies! |
|
|
| ## Citation |
| ``` |
| @article{shao2025reasonir, |
| title={ReasonIR: Training Retrievers for Reasoning Tasks}, |
| author={Rulin Shao and Rui Qiao and Varsha Kishore and Niklas Muennighoff and Xi Victoria Lin and Daniela Rus and Bryan Kian Hsiang Low and Sewon Min and Wen-tau Yih and Pang Wei Koh and Luke Zettlemoyer}, |
| year={2025}, |
| journal={arXiv preprint arXiv:2504.20595}, |
| url={https://arxiv.org/abs/2504.20595}, |
| } |
| ``` |
