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OGAI-r1

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retrieval-augmented-generation
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energy-ai
tiny-r1-32b
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OGAI-r1
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---
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}
}
```