metadata

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)

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

pip install torch transformers accelerate bitsandbytes

2️⃣ Load the Model in 4-bit Mode

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 – FP16 model for full-accuracy inference.
8-bit Version – Optimized for medium-memory GPUs.
GainEnergy AI Platform – Explore AI-powered drilling automation.

📚 Citing OGAI-8x7B-4bit

@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}
}