---
license: apache-2.0
datasets:
- GainEnergy/SMoE-Training
- GainEnergy/reasoner
- GainEnergy/ogai-8x7B
- GainEnergy/oilandgas-engineering-dataset
- GainEnergy/ogdataset
- GainEnergy/upstrimacentral
- open-r1/OpenR1-Math-220k
- unsloth/LaTeX_OCR
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
tags:
- oil-gas
- drilling-engineering
- mixtral-8x7b
- lora
- fine-tuned
- energy-ai
- pragmatic-ai
- gguf
- text-generation-inference
- text-generation
model-index:
- name: OGAI-8x7B
  results:
  - task:
      type: text-generation
      name: Drilling Engineering AI
    dataset:
      name: GainEnergy Oil & Gas Corpus
      type: custom
    metrics:
    - name: Drilling Calculations Accuracy
      type: accuracy
      value: 95.2
    - name: Engineering Document Retrieval Precision
      type: precision
      value: 91.8
    - name: Context Retention
      type: contextual-coherence
      value: High
  variants:
  - name: OGAI-8x7B-4bit
    pipeline_tag: text-generation
    repo_name: GainEnergy/ogai-8x7b-4bit
  - name: OGAI-8x7B-8bit-32k
    pipeline_tag: text-generation
    repo_name: GainEnergy/ogai-8x7b-8bit-32k
  - name: OGAI-8x7b-Q4_K_M-GGUF
    pipeline_tag: text-generation
    repo_name: GainEnergy/OGAI-8x7b-Q4_K_M-GGUF
library_name: transformers
language:
- en
widget:
- text: >-
    User: What is the recommended mud weight for drilling a well with a
    formation pressure of 8,000 psi?

    AI:
  example_title: Mud Weight Calculation
- text: >-
    User: Explain the differences between rotary steerable systems and
    conventional directional drilling tools.

    AI:
  example_title: Directional Drilling Technologies
- text: >-
    User: How does drilling fluid viscosity impact hole cleaning efficiency in
    horizontal wells?

    AI:
  example_title: Drilling Fluid Rheology
- text: >-
    User: What are the key factors affecting wellbore stability during drilling
    operations?

    AI:
  example_title: Wellbore Stability Analysis
- text: >-
    User: Describe the process of well control in the event of a kick during
    drilling operations.

    AI:
  example_title: Well Control Procedures
pipeline_tag: text-generation
---

# OGAI-8x7B: Oil & Gas AI Model for Drilling Process Optimization

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

## Model Description

**OGAI-8x7B** is a **LoRA fine-tuned Mixtral-8x7B model**, engineered for **oil and gas engineering applications**. This version has been optimized for **drilling process automation, technical document understanding, and engineering problem-solving**.

The model is a core component of **GainEnergy's Upstrima AI Platform**, which provides **pragmatic AI agents, advanced workflows, and retrieval-augmented generation (RAG)-enhanced document processing**.

## Technical Architecture

### Base Model Specifications
- **Architecture**: Mixtral-8x7B Sparse Mixture of Experts (SMoE)
- **Parameters**: 8 experts with 7B parameters each (46.7B total parameters, 12.9B active per token)
- **Context Length**: Extended to 32,768 tokens for handling technical documentation
- **Attention Mechanism**: Sliding Window Attention with specialized oil & gas technical vocabulary

### Fine-tuning Approach
- **Method**: Low-Rank Adaptation (LoRA) with rank 16
- **Training Dataset**: 2.8M specialized oil & gas engineering datapoints (800K drilling-specific)
- **Hardware**: Trained on 16x NVIDIA A100 80GB GPUs
- **Training Time**: 2,800 GPU hours
- **Special Features**: Enhanced numerical precision for engineering calculations with reduced hallucination rates

### Performance Optimizations
- **Quantization**: Custom 4-bit and 8-bit quantization schemes preserving numerical accuracy
- **Inference Speed**: Optimized KV cache management for real-time drilling advisory
- **Memory Footprint**: Reduced to 12GB with 4-bit quantization while maintaining 95%+ calculation accuracy
- **Speculative Decoding**: Implemented for 2.7x faster response generation with technical queries

## Deployment-Optimized Versions

For flexible deployment options, we provide **quantized versions** of this model:

- **[4-bit Quantized Version (NF4)](https://huggingface.co/GainEnergy/ogai-8x7b-4bit)** – Lower memory usage with efficient performance.
- **[8-bit Quantized Version (Int8)](https://huggingface.co/GainEnergy/ogai-8x7b-8bit-32k)** – Balanced model size and precision.
- **[GGUF Version for llama.cpp](https://huggingface.co/GainEnergy/OGAI-8x7b-Q4_K_M-GGUF)** – Optimized for CPU-based inference and edge computing.

### Using the GGUF Model with llama.cpp

To run OGAI-8x7B using llama.cpp, use the following command:

```bash
./server \
  --gguf-file-name ogai-8x7b-q4_k_m.gguf \
  --repo-slug GainEnergy/OGAI-8x7b-Q4_K_M-GGUF \
  --np 8
```

## Deployment Options

This model supports one-click deployment to various platforms directly from the Hugging Face Hub:

### Hugging Face Inference Endpoints
Click the "Deploy" button and select "Inference Endpoints" to deploy on Hugging Face's managed infrastructure.

### Local Deployment with vLLM
```bash
python -m vllm.entrypoints.openai.api_server \
  --model GainEnergy/ogai-8x7b \
  --tensor-parallel-size 2
```

## OGAI Model Family & Expansion Roadmap

OGAI-8x7B is part of the OGAI Model Family, designed for various energy-sector applications.

| **Model Name** | **Base Model** | **Fine-Tuning** | **Domain Focus** |
|----------------|----------------|-----------------|------------------|
| **OGAI 3.1 Engineer** | Custom Framework | Full fine-tuning | AI-powered Oil & Gas Engineering Assistance |
| **OGAI-Quantum** | Hybrid AI-Quantum | Hybrid Fine-Tuning | Reservoir Simulation & Seismic Data Processing |
| **OGAI-R1** | TinyR1-32B | Fine-tuned | Engineering AI Reasoning & Logical Analysis |
| **OGMOE** | Mixtral-8x7B MoE | Fine-tuned | Mixture of Experts (MoE) for Drilling Optimization |
| **OGAI-8x22B** | Mixtral-8x22B | Full fine-tuning | High-Performance LLM for Engineering Reasoning |
| **OGAI-8x7B** | Mixtral-8x7B | LoRA fine-tuning | Drilling Optimization, Well Planning & Document RAG |

## How to Use

### Run Inference in Python

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "GainEnergy/ogai-8x7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Calculate the required casing depth for a well in a high-pressure formation."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Citing OGAI-8x7B

```
@article{ogai8x7b2025,
  title={OGAI-8x7B: An AI Model for Oil & Gas Drilling Engineering},
  author={GainEnergy AI Team},
  year={2025},
  publisher={Hugging Face Models}
}
```