Hugging Face's logo

GainEnergy
/
OGAI-r1

Transformers
oil-gas
drilling-engineering
retrieval-augmented-generation
finetuned
energy-ai
tiny-r1-32b
lora
Eval Results
Inference Endpoints
Model card Files Community
OGAI-r1 / README.md
tommytracx's picture
tommytracx
Update README.md
d16b1c5 verified
preview code
|
raw
history blame contribute delete
5.23 kB
metadata
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
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 

pip install torch transformers accelerate bitsandbytes

2️⃣ Load the Model 

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

📚 Citing OGAI-R1 

@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}
}