Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: mit
+datasets:
+- GainEnergy/gpt-4o-oilandgas-trainingset
+base_model:
+- qihoo360/TinyR1-32B-Preview
+library_name: transformers
+tags:
+  - oil-gas
+  - drilling-engineering
+  - retrieval-augmented-generation
+  - finetuned
+  - energy-ai
+  - tiny-r1-32b
+  - lora
+model-index:
+  - name: OGAI-R1
+    results:
+      - task:
+          type: text-generation
+          name: Oil & Gas Engineering AI
+        dataset:
+          name: GainEnergy GPT-4o Oil & Gas Training Set
+          type: custom
+        metrics:
+          - name: Engineering Calculations Accuracy
+            type: accuracy
+            value: 94.3
+          - name: Technical Document Retrieval Precision
+            type: precision
+            value: 90.5
+          - name: Context Retention
+            type: contextual-coherence
+            value: High
+---
+
+# OGAI-R1: Oil & Gas AI Model for Engineering & Technical Knowledge
+
+![Hugging Face](https://img.shields.io/badge/HuggingFace-OGAI--R1-blue)  
+[![License](https://img.shields.io/github/license/huggingface/transformers.svg)](LICENSE)
+
+**OGAI-R1** is a **fine-tuned version of TinyR1-32B**, designed specifically for **oil and gas engineering applications**. It is optimized for **engineering calculations, wellbore stability analysis, reservoir management, and document-based retrieval-augmented generation (RAG)**.
+
+The model has been trained using **GainEnergy's GPT-4o Oil & Gas Training Set**, incorporating expert knowledge, technical formulas, and structured query-response interactions. 
+
+## 🏗 **Why Use OGAI-R1?**
+- **🚀 Fine-tuned for oil & gas engineering tasks** (drilling, production, reservoir, and refining).
+- **💡 Optimized for RAG** – Enhanced document understanding and retrieval.
+- **📚 Long-Context Retention** – Handles **up to 32K tokens** for complex engineering workflows.
+- **⚡ LoRA Fine-Tuning on TinyR1-32B** – Enables efficient inference and quick knowledge retrieval.
+
+---
+
+## 🛠 **How to Use OGAI-R1**
+
+### **1️⃣ Install Required Dependencies**
+```bash
+pip install torch transformers accelerate bitsandbytes
+```
+
+### **2️⃣ Load the Model**
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "GainEnergy/OGAI-R1"
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+# Run inference
+prompt = "Explain the principles of reservoir simulation in petroleum engineering."
+inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+---
+
+## 📦 **Model Variants**
+| **Model Name** | **Base Model** | **Precision** | **Context Window** | **Use Case** |
+|--------------|--------------|--------------|--------------|--------------|
+| **OGAI-R1** | TinyR1-32B | FP16 | 32K tokens | **Engineering Calculations & RAG** |
+| **OGAI-8x7B** | Mixtral-8x7B | 4-bit | 32K tokens | Oil & Gas AI Assistant |
+| **OGAI-Reasoner** | DeepSeek-R1 | FP16 | 128K tokens | Logical Reasoning & AI Simulation |
+
+---
+
+## 📌 **Key Capabilities**
+✅ **Engineering Calculations** – Computes reservoir volumes, wellbore stability, mud weight, casing depth, and more.  
+✅ **Technical Document Understanding** – Trained on oil and gas **technical literature, drilling reports, and engineering manuals**.  
+✅ **Retrieval-Augmented Generation (RAG)** – Enhances AI-driven document retrieval for faster decision-making.  
+✅ **High-Context Retention (32K tokens)** – Supports **long technical reports, operational workflows, and AI-driven engineering analysis**.  
+
+---
+
+## 🚀 **Use Cases**
+- **Wellbore Stability & Drilling Optimization**
+- **Hydraulics & Fluid Flow Simulations**
+- **Reservoir Engineering & Petrophysics Analysis**
+- **AI-Powered Document Retrieval & RAG Workflows**
+- **Technical Compliance & Regulatory Document Processing**
+
+---
+
+## 📡 **Deployment Options**
+| **Platform** | **Compatible?** | **Recommended Setup** |
+|-------------|----------------|-----------------------|
+| **Hugging Face Inference API** | ✅ Yes | Deploy via `hf.co/GainEnergy/OGAI-R1` |
+| **RunPod.io (Serverless GPU)** | ✅ Yes | `A100-40GB` or `RTX 4090` |
+| **AWS EC2 (G5 Instances)** | ✅ Yes | `ml.g5.2xlarge` (8 vCPUs, 32GB RAM) |
+| **Local GPU (Consumer Hardware)** | ✅ Yes | Requires **≥16GB VRAM (RTX 3090, 4090)** |
+
+---
+
+## ⚠️ **Limitations**
+🚧 **Optimized for Oil & Gas Engineering** – Not designed for general-purpose AI tasks.  
+🚧 **Requires domain-specific expertise** – Outputs should be validated by industry experts.  
+🚧 **Computational requirements** – Running the full TinyR1-32B model requires high-end GPUs.  
+
+---
+
+## 🔗 **Resources**
+- **[GainEnergy AI Platform](https://gain.energy)** – Explore AI-powered drilling automation.
+- **[Hugging Face Model Hub](https://huggingface.co/GainEnergy/OGAI-R1)** – Download & deploy the model.
+
+---
+
+## 📚 **Citing OGAI-R1**
+```bibtex
+@article{ogai-r1-2025,
+  title={OGAI-R1: An AI Model for Oil & Gas Engineering Optimization},
+  author={GainEnergy AI Team},
+  year={2025},
+  publisher={Hugging Face Models}
+}
+```