abdoelsayed
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dear-8b-reranker-ce-lora-v1

@@ -1,202 +1,175 @@
 ---
-base_model: meta-llama/Llama-3.1-8B
 library_name: peft
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.13.2

 ---
+language:
+- en
+license: mit
 library_name: peft
+tags:
+- reranking
+- information-retrieval
+- pointwise
+- lora
+- peft
+- binary-cross-entropy
+base_model: meta-llama/Llama-3.1-8B
+datasets:
+- Tevatron/msmarco-passage
+- abdoelsayed/DeAR-COT
+pipeline_tag: text-classification
 ---
+# DeAR-8B-Reranker-CE-LoRA-v1
+## Model Description
+**DeAR-8B-Reranker-CE-LoRA-v1** is a LoRA (Low-Rank Adaptation) adapter for neural reranking trained with Binary Cross-Entropy loss. This lightweight adapter requires only ~100MB of storage and can be applied to LLaMA-3.1-8B to achieve near full-model performance with minimal overhead.
 ## Model Details
+- **Model Type:** LoRA Adapter for Pointwise Reranking
+- **Base Model:** meta-llama/Llama-3.1-8B
+- **Adapter Size:** ~100MB
+- **Training Method:** LoRA with Binary Cross-Entropy + Knowledge Distillation
+- **LoRA Rank:** 16
+- **LoRA Alpha:** 32
+- **Trainable Parameters:** 67M (0.8% of total)
+## Key Features
+✅ **Ultra Lightweight:** Only 100MB storage
+✅ **Efficient:** 3x faster training than full fine-tuning
+✅ **High Performance:** 98% of full model accuracy
+✅ **Easy Integration:** Simple adapter loading
+✅ **Classification-based:** Binary relevance prediction
+## Usage
+### Load and Use
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel, PeftConfig
+# Load LoRA adapter
+adapter_path = "abdoelsayed/dear-8b-reranker-ce-lora-v1"
+config = PeftConfig.from_pretrained(adapter_path)
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+# Load base model
+base_model = AutoModelForSequenceClassification.from_pretrained(
+    config.base_model_name_or_path,
+    num_labels=1,
+    torch_dtype=torch.bfloat16
+)
+# Load and merge LoRA
+model = PeftModel.from_pretrained(base_model, adapter_path)
+model = model.merge_and_unload()
+model.eval().cuda()
+# Score query-document pair
+query = "What is machine learning?"
+document = "Machine learning is a subset of artificial intelligence..."
+inputs = tokenizer(
+    f"query: {query}",
+    f"document: {document}",
+    return_tensors="pt",
+    truncation=True,
+    max_length=228,
+    padding="max_length"
+)
+inputs = {k: v.cuda() for k, v in inputs.items()}
+with torch.no_grad():
+    score = model(**inputs).logits.squeeze().item()
+print(f"Relevance score: {score}")
+```
+### Batch Reranking
+```python
+@torch.inference_mode()
+def rerank(tokenizer, model, query: str, documents, batch_size=64):
+    scores = []
+    device = next(model.parameters()).device
+    for i in range(0, len(documents), batch_size):
+        batch = documents[i:i + batch_size]
+        queries = [f"query: {query}"] * len(batch)
+        docs = [f"document: {title} {text}" for title, text in batch]
+        inputs = tokenizer(queries, docs, return_tensors="pt",
+                         truncation=True, max_length=228, padding=True)
+        inputs = {k: v.to(device) for k, v in inputs.items()}
+        logits = model(**inputs).logits.squeeze(-1)
+        scores.extend(logits.cpu().tolist())
+    return sorted(enumerate(scores), key=lambda x: x[1], reverse=True)
+```
 ## Training Details
+### LoRA Configuration
+```python
+{
+    "r": 16,
+    "lora_alpha": 32,
+    "target_modules": ["q_proj", "v_proj", "k_proj", "o_proj",
+                       "gate_proj", "up_proj", "down_proj"],
+    "lora_dropout": 0.05,
+    "bias": "none",
+    "task_type": "SEQ_CLS"
+}
+```
+### Training Hyperparameters
+- **Learning Rate:** 1e-4
+- **Batch Size:** 4
+- **Gradient Accumulation:** 2
+- **Epochs:** 2
+- **Hardware:** 4x A100 (40GB)
+- **Training Time:** ~12 hours
+- **Memory:** ~28GB per GPU
+## Advantages
+| Feature | LoRA | Full Model |
+|---------|------|------------|
+| Storage | 100MB | 16GB |
+| Training Time | 12h | 34h |
+| Performance | 98% | 100% |
+| Memory | 28GB | 38GB |
+## Related Models
+- [DeAR-8B-CE](https://huggingface.co/abdoelsayed/dear-8b-reranker-ce-v1) - Full model
+- [DeAR-8B-RankNet-LoRA](https://huggingface.co/abdoelsayed/dear-8b-reranker-ranknet-lora-v1) - RankNet variant
+- [Teacher Model](https://huggingface.co/abdoelsayed/llama2-13b-rankllama-teacher)
+## Citation
+```bibtex
+@article{abdallah2025dear,
+  title={DeAR: Dual-Stage Document Reranking with Reasoning Agents via LLM Distillation},
+  author={Abdallah, Abdelrahman and Mozafari, Jamshid and Piryani, Bhawna and Jatowt, Adam},
+  journal={arXiv preprint arXiv:2508.16998},
+  year={2025}
+}
+```
+## License
+MIT License
+## More Information
+- **GitHub:** [DataScienceUIBK/DeAR-Reranking](https://github.com/DataScienceUIBK/DeAR-Reranking)
+- **Paper:** [arXiv:2508.16998](https://arxiv.org/abs/2508.16998)
+- **Collection:** [DeAR Models](https://huggingface.co/collections/abdoelsayed/dear-reranking)