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OGAI-STEM-7B

Text Generation
Transformers
Safetensors
GGUF
English
mistral
oil-gas
drilling-engineering
mathstral-7b
lora
fine-tuned
energy-ai
pragmatic-ai
text-generation-inference
conversational
Eval Results
Inference Endpoints
Model card Files
OGAI-STEM-7B / 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/Mathstral-7B-v0.1
tags:
  - oil-gas
  - drilling-engineering
  - mathstral-7b
  - lora
  - fine-tuned
  - energy-ai
  - pragmatic-ai
  - gguf
  - text-generation-inference
  - text-generation
model-index:
  - name: OGAI-STEM-7B
    results:
      - task:
          type: text-generation
          name: Engineering AI for Oil & Gas
        dataset:
          name: GainEnergy Oil & Gas Corpus
          type: custom
        metrics:
          - name: Engineering Calculations Accuracy
            type: accuracy
            value: 94.5
          - name: Scientific Computation Precision
            type: precision
            value: 92.3
          - name: Context Retention
            type: contextual-coherence
            value: High
    variants:
      - name: OGAI-STEM-7B-GGUF
        pipeline_tag: text-generation
        repo_name: GainEnergy/OGAI-STEM-7B-GGUF
library_name: transformers
language:
  - en
widget:
  - text: >-
      User: What is the pressure drop in a horizontal pipeline for crude oil
      transport?

      AI:
    example_title: Pipeline Pressure Drop Calculation
  - text: >-
      User: Explain the differences between gas lift and electric submersible
      pumps in artificial lift.

      AI:
    example_title: Artificial Lift Methods
  - text: |-
      User: How do you calculate mud weight for deepwater drilling?
      AI:
    example_title: Mud Weight Calculation
  - text: >-
      User: Describe the steps to optimize wellbore stability in unconventional
      reservoirs.

      AI:
    example_title: Wellbore Stability Optimization
pipeline_tag: text-generation

OGAI-STEM-7B: AI-Powered Engineering Model for Oil & Gas Calculations

Hugging Face
License

Model Description

OGAI-STEM-7B is a LoRA fine-tuned Mathstral-7B model, designed specifically for oil and gas engineering, scientific computing, and technical problem-solving. It is optimized for numerical accuracy, complex engineering calculations, and technical document understanding.

The model is an integral part of GainEnergy's Upstrima AI Platform, enhancing workflows with pragmatic AI agents, scientific computing tools, and retrieval-augmented generation (RAG)-based document analysis.

Technical Architecture

Base Model Specifications

  • Architecture: Mathstral-7B (Mistral fine-tuned for advanced math reasoning)
  • Parameters: 7B
  • Context Length: 32,768 tokens for long-form scientific queries
  • Mathematical Precision: Enhanced for oil & gas engineering computations

Fine-tuning Approach

  • Method: Low-Rank Adaptation (LoRA) with rank 64
  • Training Dataset: 3.2M datapoints from specialized oil & gas engineering sources
  • Hardware: Trained on 8x NVIDIA A100 80GB GPUs
  • Training Time: 2,200 GPU hours
  • Special Features: Improved accuracy in fluid mechanics, pressure drop, and geomechanics calculations

Performance Optimizations

  • Quantization: 4-bit and 8-bit versions optimized for low-memory inference
  • Inference Speed: Tuned KV cache management for real-time engineering computations
  • Memory Footprint: Runs efficiently on 12GB VRAM with 4-bit quantization
  • Reduced Hallucinations: Domain-specific fine-tuning minimizes incorrect scientific results

Deployment-Optimized Versions

Version Memory Requirement Performance
OGAI-STEM-7B-GGUF CPU optimized Suitable for edge computing

Local Deployment with vLLM 

python -m vllm.entrypoints.openai.api_server \
  --model GainEnergy/ogai-stem-7b \
  --tensor-parallel-size 2

How to Use

Run Inference in Python 

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "GainEnergy/ogai-stem-7b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Calculate the pressure drop in a 500m pipeline with a 10,000 BPD flow rate."
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-STEM-7B 

@article{ogai_stem_7b_2025,
  title={OGAI-STEM-7B: AI Model for Oil & Gas Scientific Computing},
  author={GainEnergy AI Team},
  year={2025},
  publisher={Hugging Face Models}
}