---
license: mit
datasets:
- GainEnergy/ogai-8x7B
- GainEnergy/oilandgas-engineering-dataset
- GainEnergy/ogdataset
- GainEnergy/upstrimacentral
base_model:
- mistralai/Mixtral-8x7B-Instruct-v0.1
library_name: transformers
tags:
  - oil-gas
  - drilling-engineering
  - mixtral-8x7b
  - lora
  - fine-tuned
  - energy-ai
  - retrieval-augmented-generation
  - 4bit
  - quantized
model-index:
  - name: OGAI-8x7B-4bit
    results:
      - task:
          type: text-generation
          name: Drilling Engineering AI (Quantized)
        dataset:
          name: GainEnergy Oil & Gas Corpus
          type: custom
        metrics:
          - name: Drilling Calculations Accuracy
            type: accuracy
            value: 92.5
          - name: Engineering Document Retrieval Precision
            type: precision
            value: 89.8
          - name: Context Retention
            type: contextual-coherence
            value: Medium-High
    variants:
      - name: OGAI-8x7B
        pipeline_tag: text-generation
        repo_name: GainEnergy/ogai-8x7b
      - name: OGAI-8x7B-4bit
        pipeline_tag: text-generation
        repo_name: GainEnergy/ogai-8x7b-4bit
      - name: OGAI-8x7B-8bit
        pipeline_tag: text-generation
        repo_name: GainEnergy/ogai-8x7b-8bit
---

# OGAI-8x7B-4bit: Oil & Gas AI Model for Drilling Optimization (Quantized)

![Hugging Face](https://img.shields.io/badge/HuggingFace-OGAI--8x7B--4bit-orange)  
[![License](https://img.shields.io/github/license/huggingface/transformers.svg)](LICENSE)

**OGAI-8x7B-4bit** is a **quantized (4-bit) version** of **OGAI-8x7B**, optimized for **deployment in resource-constrained environments** while maintaining strong performance for **oil and gas drilling engineering tasks**.

The model is **fine-tuned from Mixtral-8x7B**, leveraging a **comprehensive dataset** of **technical literature, engineering workflows, and drilling reports**. It is part of **GainEnergy's Upstrima AI platform**, providing **intelligent AI agents, automated workflows, and document-based retrieval-augmented generation (RAG).**

---

## 🏗 **Why Use the 4-bit Model?**
- **🚀 Lower VRAM Usage** – Runs on **consumer GPUs** with **less than 16GB VRAM**.
- **💡 Efficient Inference** – Optimized using **BitsAndBytes 4-bit NF4 quantization**.
- **📚 Long-Context Understanding** – Retains **up to 32K tokens** for technical documents.
- **🔬 Expert-Focused Fine-Tuning** – Specifically optimized for **drilling operations**.

---

## 🛠 **Using the OGAI-8x7B-4bit Model**

### **1️⃣ Install Required Dependencies**
```bash
pip install torch transformers accelerate bitsandbytes
```

### **2️⃣ Load the Model in 4-bit Mode**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "GainEnergy/ogai-8x7b-4bit"
bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype="float16")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Load model with quantization config
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    quantization_config=bnb_config
)

# Run inference
prompt = "Explain the impact of mud weight on wellbore stability."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

---

## 📌 **Key Capabilities**
✅ **Drilling Calculations & Well Planning** – Computes wellbore stability, casing depth, and mud weight optimization.  
✅ **Engineering Document Processing (RAG)** – Enables document-based AI workflows for drilling reports and operational manuals.  
✅ **AI-Powered Drilling Workflows** – Integrates with **Upstrima AI** for automated engineering analysis.  
✅ **Optimized for Low VRAM GPUs** – Runs on **RTX 4090, A100, and consumer-grade GPUs**.  

---

## 🚀 **Use Cases**
🔹 **Drilling Engineering** – Automated analysis of wellbore stability, casing designs, and drilling hydraulics.  
🔹 **Real-time Optimization** – Assists drilling teams in **reducing non-productive time (NPT)** and improving efficiency.  
🔹 **AI-Driven Document Retrieval** – Enables **retrieval-augmented generation (RAG)** for drilling compliance and regulatory reporting.  
🔹 **Seamless Integration** – Deployable within **Upstrima AI**, GainEnergy's intelligent AI platform for the oil and gas industry.  

---

## 🔗 **Resources**
- **[Full Precision Model](https://huggingface.co/GainEnergy/ogai-8x7b)** – FP16 model for full-accuracy inference.
- **[8-bit Version](https://huggingface.co/GainEnergy/ogai-8x7b-8bit)** – Optimized for medium-memory GPUs.
- **[GainEnergy AI Platform](https://gain.energy)** – Explore AI-powered drilling automation.

---

## 📚 **Citing OGAI-8x7B-4bit**
```bibtex
@article{ogai8x7b4bit2025,
  title={OGAI-8x7B-4bit: A Quantized AI Model for Oil & Gas Drilling Engineering},
  author={GainEnergy AI Team},
  year={2025},
  publisher={Hugging Face Models}
}
```