Update README.md

README.md

@@ -4,8 +4,96 @@ base_model:
 - mistralai/Mistral-7B-Instruct-v0.3
 ---
 
-Github link: https://github.com/RyanWangZf/LEADS
+# **LEADS-Mistral-7B-v1: A Fine-Tuned LLM for Systematic Review Literature Mining**
 
-Instruction data link: https://huggingface.co/datasets/zifeng-ai/LEADSInstruct
 
-Paper link: https://arxiv.org/abs/2501.16255
+## **Model Overview**
+**LEADS-Mistral-7B-v1** is a fine-tuned version of the **Mistral-7B-Instruct** model, specifically designed for systematic review literature mining tasks. This model has been optimized to assist researchers and clinicians in automating key stages of systematic review methodology, including **literature search, citation screening, and data extraction**.
+
+The model was trained on **LEADSInstruct**, the largest publicly available instruction dataset for systematic reviews, comprising **633,759 instruction data points**. This fine-tuning process enhances its ability to process biomedical literature, extract key study characteristics, and support AI-driven evidence synthesis.
+
+## **Key Features**
+✅ **Fine-tuned for Literature Mining**: Specialized for systematic review tasks, leveraging PubMed and ClinicalTrials.gov data.  
+✅ **Multi-Step Systematic Review Support**: Handles search query generation, study eligibility assessment, and detailed data extraction.  
+✅ **Instruction-Tuned for Task-Specific Adaptation**: Trained using structured input-output instructions for high precision.  
+✅ **Optimized for Biomedical Text**: Evaluated against proprietary and open-source models for superior domain-specific performance.  
+
+---
+
+## **Model Details**
+- **Base Model**: [Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)  
+- **Fine-Tuning Dataset**: [LEADSInstruct](https://huggingface.co/datasets/zifeng-ai/LEADSInstruct)  
+- **Model Size**: 7.25B parameters  
+- **Tensor Type**: BF16  
+- **License**: MIT  
+
+### **Training & Benchmarking**
+The model was trained using systematic reviews and clinical trial data, covering six major subtasks:
+
+1. **Search Query Generation** – Maximizing study retrieval effectiveness.  
+2. **Study Eligibility Prediction** – Automated citation screening for relevance.  
+3. **Study Characteristics Extraction** – Extracting structured metadata from studies.  
+4. **Trial Result Extraction** – Capturing key outcomes from clinical trials.  
+5. **Participant Statistics Extraction** – Extracting sample size and demographic information.  
+6. **Arm Design Extraction** – Identifying intervention and control groups.  
+
+### **Evaluation & Performance**
+LEADS-Mistral-7B-v1 was benchmarked against a variety of proprietary and open-source LLMs, including:
+- **GPT-4o**, **GPT-3.5**, **Haiku-3** (Proprietary models)  
+- **Mistral-7B**, **Llama-3** (General-purpose open-source LLMs)  
+- **BioMistral**, **MedAlpaca** (Medical-specific LLMs)  
+
+The model demonstrated **state-of-the-art performance** in literature mining tasks, significantly improving **data extraction accuracy** and **study screening recall**, while reducing manual effort and time cost.
+
+---
+
+## **Usage**
+### **Loading the Model**
+To use the model in Hugging Face's `transformers` library:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "zifeng-ai/leads-mistral-7b-v1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+```
+
+### ***Examples***
+
+```python
+from leads.api import search_query_generation
+
+pico = {
+    "population": "Adults with type 2 diabetes",
+    "intervention": "SGLT2 inhibitors",
+    "comparison": "GLP-1 receptor agonists",
+    "outcome": "Cardiovascular outcomes and glycemic control"
+}
+
+search_query = search_query_generation(**pico)
+print(search_query)
+```
+
+---
+
+## **Resources**
+- **📄 Paper**: [LEADS: AI-Driven Literature Mining for Systematic Reviews](https://arxiv.org/abs/2501.16255)  
+- **📂 Dataset**: [LEADSInstruct on Hugging Face](https://huggingface.co/datasets/zifeng-ai/LEADSInstruct)  
+- **🔗 GitHub**: [LEADS Repository](https://github.com/RyanWangZf/LEADS)  
+
+---
+
+## **License & Citation**
+This model is released under the **MIT License**, allowing unrestricted research and application use.
+
+If you use **LEADS-Mistral-7B-v1**, please cite:
+
+```
+@article{wang2025foundation,
+  title={A foundation model for human-AI collaboration in medical literature mining},
+  author={Wang, Zifeng and Cao, Lang and Jin, Qiao and Chan, Joey and Wan, Nicholas and Afzali, Behdad and Cho, Hyun-Jin and Choi, Chang-In and Emamverdi, Mehdi and Gill, Manjot K and others},
+  journal={arXiv preprint arXiv:2501.16255},
+  year={2025}
+}
+```