README.md

---
base_model:
- meta-llama/Llama-3.1-8B
language:
- en
license: cc-by-sa-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.

- Repository: https://github.com/facebookresearch/ReasonIR
- Paper: https://arxiv.org/abs/2504.20595

## 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, instruction=query_instruction)
doc_emb = model.encode(document, instruction=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 discrepancy 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.

## 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}, 
}
```