OGAI 3.1 Engineer

Model Author: Gain.Energy
Lead Developers: Dr. Vlad Karén Payrazyan, CEO and Founder at Gain.Energy; Tommy Xaypanya, Lead AI Scientist and Developer at Gain.Energy
Date Created: November 12, 2024

Overview

OGAI 3.1 Engineer is a large language model built on NVIDIA’s Llama-3.1-Nemotron-70B-Instruct-HF and customized specifically for the oil and gas industry, with a focus on drilling engineering. This model has been fine-tuned to understand and process technical calculations, interpret engineering documents, and generate domain-specific insights, making it a valuable asset for engineers and analysts.

Applications:

Complex engineering calculations
Document interpretation and summarization
Drilling optimization and safety compliance
Collaborative, real-time engineering workspaces

Model Details

Base Model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
Parameter Count: 70 billion
Architecture: Transformer-based
Input Format: Text prompts up to 128k tokens
Output Format: Text responses up to 4k tokens

Revision History

Revision 1.0 - Initial Release (November 12, 2024)

Base Model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
Custom Training: Focused on oil and gas drilling engineering documents, industry standards, technical calculations, and safety protocols.
Training Data:
- Industry-specific manuals, textbooks, and historical operational data.
- Preprocessed datasets to ensure consistency and confidentiality.
Fine-Tuning Techniques:
- Low-Rank Adaptation (LoRA): Applied LoRA for efficient parameter fine-tuning.
- Retrieval-Augmented Generation (RAG): Integrated for real-time knowledge base retrieval.
- Prompt Engineering: Crafted domain-specific prompts for enhanced accuracy.

Installation

To install and run OGAI 3.1 Engineer, you’ll need:

Python 3.9 or higher
PyTorch 1.12 or higher
CUDA 11.8 for GPU support

Clone the Repository

git clone https://huggingface.co./gain-energy/OGAI-3.1-Engineer
cd OGAI-3.1-Engineer
pip install -r requirements.txt

Usage Example

Here is an example code to load and interact with OGAI 3.1 Engineer:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "gain-energy/OGAI-3.1-Engineer"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Calculate the mud weight required for a well with a true vertical depth of 15,000 feet and formation pressure of 10,000 psi."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=200)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)

Model Performance and Evaluation

The model was benchmarked on several evaluation metrics relevant to oil and gas applications:

Domain-Specific Accuracy: 88% accuracy in answering technical questions.
Calculation Precision: Improved calculation accuracy by 90% over baseline.
Benchmark Scores:
Arena Hard: 86.5%
AlpacaEval 2.0 LC: 60%
GPT-4-Turbo MT-Bench: Score of 9.1

Training and Fine-Tuning

Training Hardware: NVIDIA DGX systems with A100 GPUs (80 GB VRAM per GPU).
Training Parameters: Batch size of 8 per GPU, learning rate of 1e-4 with a cosine decay, 3 epochs.
Optimization Algorithm: AdamW with weight decay.

Intended Use and Limitations

Intended Use

OGAI 3.1 Engineer is intended for professionals in the oil and gas industry, particularly those focused on drilling operations, safety compliance, and technical calculations. Its specialized training enables it to handle domain-specific terminology, calculations, and documentation with a high degree of accuracy.

Limitations

Numerical Computation: While enhanced for complex calculations, the model may require external computational tools for highly intricate numerical tasks.
Generalization: The model may not perform optimally on general knowledge topics outside its fine-tuned oil and gas domain.

License

This model is released under the Apache License 2.0. Please see the LICENSE file for more details.

Acknowledgments

Special thanks to NVIDIA AI Research for the development of the base model and to the Gain.Energy team for domain expertise and support in model fine-tuning and evaluation.

Contact Information

For support, inquiries, or collaboration opportunities, please contact:

Tommy Xaypanya Lead AI Scientist and Developer at Gain.Energy Email: [email protected]
Dr. Vlad Karén Payrazyan CEO and Founder at Gain.Energy Email: [email protected]

model-index:

name: OGAI 3.1 Engineer results:
- task: type: text-generation dataset: name: oil_gas_docs type: GainEnergy-OilGasDocs metrics:
  - name: Domain-Specific Accuracy type: accuracy value: 88.0 source: name: Gain Energy Internal Evaluation url: https://gain.energy/evaluations/ogai-3-1-engineer
- task: type: text-generation dataset: name: technical_calculations type: TechnicalCalculations-OilGas metrics:
  - name: Calculation Precision type: precision value: 90.0 source: name: Gain Energy Internal Evaluation url: https://gain.energy/evaluations/ogai-3-1-engineer
- task: type: text-generation dataset: name: arena_hard type: arena_hard metrics:
  - name: Arena Hard type: helpfulness and alignment value: 86.5 source: name: Gain Energy Internal Evaluation url: https://gain.energy/evaluations/ogai-3-1-engineer
- task: type: text-generation dataset: name: alpaca_eval_2_lc type: AlpacaEval 2.0 Length Controlled metrics:
  - name: AlpacaEval 2.0 Length Controlled (LC) type: length-controlled value: 60.0 source: name: Gain Energy Internal Evaluation url: https://gain.energy/evaluations/ogai-3-1-engineer
- task: type: text-generation dataset: name: gpt_4_turbo_mt_bench type: gpt_4_turbo_mt_bench metrics:
  - name: GPT-4-Turbo MT-Bench type: reasoning and problem-solving value: 9.1 source: name: Gain Energy Internal Evaluation url: https://gain.energy/evaluations/ogai-3-1-engineer

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OGAI-3.1-Engineer

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