Update README.md

README.md

@@ -1,202 +1,297 @@
 ---
-base_model: meta-llama/Llama-3.1-8B-Instruct
+language:
+- en
+license: mit
 library_name: peft
+tags:
+- reranking
+- information-retrieval
+- listwise
+- lora
+- peft
+- generative
+base_model: meta-llama/Llama-3.1-8B
+datasets:
+- abdoelsayed/DeAR-COT
+pipeline_tag: text-generation
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# DeAR-8B-Reranker-Listwise-LoRA-v1
 
+## Model Description
 
+**DeAR-8B-Reranker-Listwise-LoRA-v1** is a LoRA adapter for listwise neural reranking. This adapter enables generative document ranking with Chain-of-Thought reasoning while requiring only ~100MB storage. It achieves near full-model performance on complex ranking tasks.
 
 ## 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 Listwise Reranking
+- **Base Model:** meta-llama/Llama-3.1-8B
+- **Adapter Size:** ~100MB
+- **Training Method:** LoRA with Supervised Fine-tuning + CoT
+- **LoRA Rank:** 16
+- **LoRA Alpha:** 32
+- **Framework:** LLaMA-Factory
+
+## Key Features
+
+✅ **Lightweight:** Only 100MB vs 16GB full model  
+✅ **CoT Reasoning:** Generates ranking explanations  
+✅ **Listwise:** Considers document relationships  
+✅ **State-of-the-Art:** Outperforms GPT-4 on NovelEval  
+✅ **Efficient:** Faster training and deployment  
+
+
+
+## Usage
+
+### Load with PEFT
+
+```python
+import torch
+from transformers import AutoTokenizer
+from peft import AutoPeftModelForCausalLM
+
+# Load LoRA adapter (automatically loads base model)
+adapter_path = "abdoelsayed/dear-8b-reranker-listwise-lora-v1"
+dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
+
+tokenizer = AutoTokenizer.from_pretrained(adapter_path, use_fast=True)
+model = AutoPeftModelForCausalLM.from_pretrained(
+    adapter_path,
+    torch_dtype=dtype,
+    device_map="auto",
+    trust_remote_code=True,
+    low_cpu_mem_usage=True
+)
+
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+
+# Prepare ranking prompt
+query = "When did Thomas Edison invent the light bulb?"
+documents = [
+    "Lightning strike at Seoul National University",
+    "Thomas Edison tried to invent a device for car but failed",
+    "Coffee is good for diet",
+    "KEPCO fixes light problems",
+    "Thomas Edison invented the light bulb in 1879",
+]
+
+doc_list = "\n".join([f"[{i}] {doc}" for i, doc in enumerate(documents)])
+prompt = f"""I will provide you with {len(documents)} passages, each indicated by a number identifier [].
+Rank the passages based on their relevance to the search query: {query}.
+
+{doc_list}
+
+Search Query: {query}.
+Rank the passages above based on their relevance to the search query. Output the ranking as a list of numbers."""
+
+# Generate ranking
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=50,
+        temperature=0.7,
+        do_sample=False,
+        pad_token_id=tokenizer.pad_token_id
+    )
+
+ranking = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
+print(f"Ranking: {ranking}")
+# Output: [4] > [1] > [0] > [3] > [2]
+```
+
+### 4-bit Quantization (Low Memory)
+
+```python
+from peft import AutoPeftModelForCausalLM
+
+# Load with 4-bit quantization
+model = AutoPeftModelForCausalLM.from_pretrained(
+    adapter_path,
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+### Complete Reranking Pipeline
+
+```python
+import re
+from typing import List
+
+class ListwiseLoRAReranker:
+    def __init__(self, adapter_path: str):
+        self.tokenizer = AutoTokenizer.from_pretrained(adapter_path, use_fast=True)
+        self.model = AutoPeftModelForCausalLM.from_pretrained(
+            adapter_path,
+            torch_dtype=torch.bfloat16,
+            device_map="auto",
+            low_cpu_mem_usage=True
+        )
+        
+        if self.tokenizer.pad_token is None:
+            self.tokenizer.pad_token = self.tokenizer.eos_token
+    
+    def create_prompt(self, query: str, documents: List[str]) -> str:
+        doc_list = "\n".join([f"[{i}] {doc[:300]}" for i, doc in enumerate(documents)])
+        return f"""I will provide you with {len(documents)} passages, each indicated by a number identifier [].
+Rank the passages based on their relevance to the search query: {query}.
+
+{doc_list}
+
+Search Query: {query}.
+Rank the passages above based on their relevance to the search query. Output the ranking as a list of numbers."""
+    
+    def parse_ranking(self, text: str, num_docs: int) -> List[int]:
+        numbers = re.findall(r'\[(\d+)\]', text)
+        ranking = [int(n) for n in numbers if int(n) < num_docs]
+        
+        # Add missing docs
+        for i in range(num_docs):
+            if i not in ranking:
+                ranking.append(i)
+        
+        return ranking[:num_docs]
+    
+    def rerank(self, query: str, documents: List[str]) -> List[int]:
+        prompt = self.create_prompt(query, documents)
+        inputs = self.tokenizer(prompt, return_tensors="pt", truncation=True, max_length=2048)
+        inputs = {k: v.to(self.model.device) for k, v in inputs.items()}
+        
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                max_new_tokens=50,
+                do_sample=False,
+                pad_token_id=self.tokenizer.pad_token_id
+            )
+        
+        output_text = self.tokenizer.decode(
+            outputs[0][inputs['input_ids'].shape[1]:],
+            skip_special_tokens=True
+        )
+        
+        return self.parse_ranking(output_text, len(documents))
+
+# Usage
+reranker = ListwiseLoRAReranker("abdoelsayed/dear-8b-reranker-listwise-lora-v1")
+ranking = reranker.rerank(query, documents)
+print(f"Ranked indices: {ranking}")
+```
 
 ## 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
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.15.0
+### LoRA Configuration
+```yaml
+lora_rank: 16
+lora_alpha: 32
+target_modules:
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+  - gate_proj
+  - up_proj
+  - down_proj
+lora_dropout: 0.05
+task_type: CAUSAL_LM
+```
+
+### Training Setup
+- **Framework:** LLaMA-Factory
+- **Dataset:** [DeAR-COT](https://huggingface.co/datasets/abdoelsayed/DeAR-COT)
+- **Learning Rate:** 1e-5
+- **Batch Size:** 4
+- **Gradient Accumulation:** 4
+- **Epochs:** 2
+- **Max Length:** 2048
+- **GPUs:** 4x A100 (80GB)
+- **Training Time:** ~24 hours (3x faster than full)
+- **Memory:** ~50GB per GPU
+
+## Advantages of LoRA
+
+| Feature | LoRA | Full Model |
+|---------|------|------------|
+| Storage | 100MB | 16GB |
+| Training Time | 24h | 72h |
+| Training Memory | 50GB | 70GB |
+| Performance | 99% | 100% |
+| Deployment | Fast | Slow |
+
+## Performance Comparison
+
+### TREC Deep Learning
+
+| Method | DL19 | DL20 | Avg |
+|--------|------|------|-----|
+| LoRA | 77.6 | 75.3 | 76.5 |
+| Full | 77.9 | 75.6 | 76.8 |
+| RankGPT-4 | 75.6 | 70.6 | 73.1 |
+
+### NovelEval
+
+| Method | NDCG@10 |
+|--------|---------|
+| **LoRA** | **90.6** |
+| Full | 91.0 |
+| GPT-4 | 87.9 |
+
+## When to Use
+
+**Best for:**
+- ✅ Resource-constrained environments
+- ✅ Multiple domain-specific versions
+- ✅ Fast experimentation
+- ✅ Complex reasoning queries
+
+**Use full model for:**
+- ❌ Absolute maximum performance
+- ❌ Single production deployment
+
+## Limitations
+
+- Slightly lower performance (-0.3 NDCG@10)
+- Still slower than pointwise models (~11s)
+- Limited to ~20-50 documents per query
+- Requires base model for inference
+
+## Related Models
+
+**Full Version:**
+- [DeAR-8B-Listwise](https://huggingface.co/abdoelsayed/dear-8b-reranker-listwise-v1)
+
+**Other LoRA:**
+- [DeAR-8B-RankNet-LoRA](https://huggingface.co/abdoelsayed/dear-8b-reranker-ranknet-lora-v1)
+- [DeAR-8B-CE-LoRA](https://huggingface.co/abdoelsayed/dear-8b-reranker-ce-lora-v1)
+
+**Resources:**
+- [DeAR-COT Dataset](https://huggingface.co/datasets/abdoelsayed/DeAR-COT)
+- [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)