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OGAI-8x7b

Text Generation
Transformers
Safetensors
GGUF
English
mixtral
oil-gas
drilling-engineering
mixtral-8x7b
lora
fine-tuned
energy-ai
pragmatic-ai
text-generation-inference
conversational
Eval Results
Inference Endpoints
Model card Files
OGAI-8x7b / README.md
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metadata
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
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:

Using the GGUF Model with llama.cpp

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

./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 

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 

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}
}