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Model: reasonir/ReasonIR-8B
Source: Original Platform

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1_Pooling/config.json

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README.md

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+---
+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}, 
+}
+```

config.json

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+{
+  "_name_or_path": "reasonir/ReasonIR-8B",
+  "architectures": [
+    "ReasonIRModel"
+  ],
+  "auto_map": {
+    "AutoModel": "modeling_reasonir_8b.ReasonIRModel"
+  },
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
+    "factor": 8.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.47.0.dev0",
+  "use_cache": true,
+  "vocab_size": 128256
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "4.0.2",
+    "transformers": "4.48.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

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