Update README.md

README.md

@@ -1,202 +1,290 @@
 ---
-base_model: meta-llama/Llama-3.2-3B
+language:
+- en
+license: mit
 library_name: peft
+tags:
+- reranking
+- information-retrieval
+- pointwise
+- lora
+- peft
+- efficient
+- ranknet
+base_model: meta-llama/Llama-3.2-3B
+datasets:
+- Tevatron/msmarco-passage
+- abdoelsayed/DeAR-COT
+pipeline_tag: text-classification
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# DeAR-3B-Reranker-RankNet-LoRA-v1
 
+## Model Description
 
+**DeAR-3B-Reranker-RankNet-LoRA-v1** is a LoRA adapter for the most efficient reranker in the DeAR family. This ultra-lightweight adapter (~40MB) achieves fast inference speeds while maintaining competitive accuracy, making it ideal for resource-constrained production environments.
 
 ## Model Details
 
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
+- **Model Type:** LoRA Adapter for Pointwise Reranking
+- **Base Model:** meta-llama/Llama-3.2-3B
+- **Adapter Size:** ~40MB
+- **Training Method:** LoRA with RankNet Loss + Knowledge Distillation
+- **LoRA Rank:** 16
+- **LoRA Alpha:** 32
+- **Trainable Parameters:** 25M (0.8% of total)
+
+## Key Features
+
+✅ **Ultra Lightweight:** Only 40MB storage  
+✅ **Fastest Inference:** 1.5s for 100 documents  
+✅ **Memory Efficient:** 10GB GPU for inference  
+✅ **Easy Deployment:** Quick adapter loading  
+✅ **Cost Effective:** Minimal compute requirements  
+
+
+
+
+## Usage
+
+### Load and Use
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from peft import PeftModel, PeftConfig
+
+# Load LoRA adapter
+adapter_path = "abdoelsayed/dear-3b-reranker-ranknet-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()
+
+# Use model
+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}")
+```
+
+### Helper Function
+
+```python
+from typing import List, Tuple
+
+def load_3b_lora_ranker(adapter_path: str):
+    """Load 3B LoRA adapter efficiently."""
+    config = PeftConfig.from_pretrained(adapter_path)
+    
+    tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    
+    base = AutoModelForSequenceClassification.from_pretrained(
+        config.base_model_name_or_path,
+        num_labels=1,
+        torch_dtype=torch.bfloat16
+    )
+    
+    model = PeftModel.from_pretrained(base, adapter_path)
+    model = model.merge_and_unload()
+    model.eval().cuda()
+    
+    return tokenizer, model
+
+# Load once
+tokenizer, model = load_3b_lora_ranker("abdoelsayed/dear-3b-reranker-ranknet-lora-v1")
+
+# Rerank function
+@torch.inference_mode()
+def rerank(tokenizer, model, query: str, docs: List[Tuple[str, str]], batch_size=128):
+    scores = []
+    device = next(model.parameters()).device
+    
+    for i in range(0, len(docs), batch_size):
+        batch = docs[i:i + batch_size]
+        queries = [f"query: {query}"] * len(batch)
+        documents = [f"document: {t} {p}" for t, p in batch]
+        
+        inputs = tokenizer(queries, documents, 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
 
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
+### 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:** 8 (larger due to lower memory)
+- **Gradient Accumulation:** 2
+- **Epochs:** 2
+- **Hardware:** 4x A100 (40GB)
+- **Training Time:** ~6 hours (3x faster than full)
+- **Memory:** ~18GB per GPU
+
+
+
+## Efficiency Comparison
+
+### Storage Efficiency
+```
+LoRA Adapter: 40MB
+Full 3B Model: 6GB
+Full 8B Model: 16GB
+Ratio: 0.67% of 3B, 0.25% of 8B
+```
+
+### Inference Speed
+```
+3B LoRA: 1.5s (100 docs)
+8B Full: 2.2s (100 docs)
+Speedup: 1.47x faster than 8B
+```
+
+### Memory Usage
+```
+3B LoRA: 10GB GPU
+3B Full: 12GB GPU
+8B Full: 18GB GPU
+```
+
+## When to Use
+
+**Best for:**
+- ✅ Extreme resource constraints
+- ✅ Multiple domain-specific versions
+- ✅ Fast iteration cycles
+- ✅ Edge deployment
+- ✅ Maximum throughput
+
+**Use full 3B for:**
+- ❌ Slightly better accuracy needed
+- ❌ No storage constraints
+
+**Use 8B for:**
+- ❌ +3 NDCG@10 accuracy gain needed
+
+## Deployment Example
+
+```python
+# Minimal memory deployment
+import torch
+from transformers import AutoModelForSequenceClassification
+from peft import PeftModel
+
+adapter_path = "abdoelsayed/dear-3b-reranker-ranknet-lora-v1"
+
+# Load with memory optimization
+model = AutoModelForSequenceClassification.from_pretrained(
+    "meta-llama/Llama-3.2-3B",
+    num_labels=1,
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    low_cpu_mem_usage=True
+)
+
+# Load adapter
+model = PeftModel.from_pretrained(model, adapter_path)
+model = model.merge_and_unload()
+model.eval()
+
+# Optional: Compile for speedup
+if hasattr(torch, 'compile'):
+    model = torch.compile(model, mode="max-autotune")
+```
+
+## Performance vs Size
+
+```
+Model Size vs NDCG@10 (TREC DL19):
+├─ Teacher-13B: 73.8 (26GB)
+├─ DeAR-8B-Full: 74.5 (16GB)
+├─ DeAR-8B-LoRA: 74.2 (100MB + base)
+├─ DeAR-3B-Full: 71.2 (6GB)
+└─ DeAR-3B-LoRA: 70.9 (40MB + base) ← This model
+
+Best Efficiency: 95% accuracy at 0.25% size of 8B!
+```
+
+## Related Models
+
+**Full Version:**
+- [DeAR-3B-RankNet](https://huggingface.co/abdoelsayed/dear-3b-reranker-ranknet-v1)
+
+**Same Size (3B):**
+- [DeAR-3B-CE-LoRA](https://huggingface.co/abdoelsayed/dear-3b-reranker-ce-lora-v1)
+
+**Larger (8B):**
+- [DeAR-8B-RankNet-LoRA](https://huggingface.co/abdoelsayed/dear-8b-reranker-ranknet-lora-v1)
+
+**Resources:**
+- [DeAR-COT Dataset](https://huggingface.co/datasets/abdoelsayed/DeAR-COT)
+- [Teacher Model](https://huggingface.co/abdoelsayed/llama2-13b-rankllama-teacher)
 
-[More Information Needed]
-### Framework versions
+## Citation
 
-- PEFT 0.13.2
+```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)