README.md

---
language: en
license: mit
tags:
  - ai
  - quantized
  - oil-and-gas
  - engineering
  - mixtral
  - causal-language-model
  - 8-bit
library_name: transformers
pipeline_tag: text-generation
datasets:
  - custom-oil-and-gas-dataset
model_type: mixtral
model_name: OGAI-8x7B-8bit-32k
inference:
  cloud_resources:
    recommended_resources:
      gpu_memory: 24GB+
      system_ram: 32GB+
    minimum_resources:
      gpu_memory: 16GB
      system_ram: 32GB
is_quantized: true
quantization_config:
  type: 8-bit
  precision: int8
base_model: Mixtral-8x7B
base_model_license: apache-2.0
domain_specific:
  primary_domain: Oil & Gas Engineering
  key_capabilities:
    - Drilling Calculations
    - Well Trajectory Optimization
    - Hydraulics Computation
    - Technical Document Processing
  knowledge_limitation: Data available up to 2025
---
# OGAI-8x7B-8bit-32k: 8-bit Quantized Oil & Gas AI Model with Extended Context

![Hugging Face](https://img.shields.io/badge/HuggingFace-OGAI--8x7B--8bit--32k-orange)  
[![License](https://img.shields.io/github/license/huggingface/transformers.svg)](LICENSE)

## Model Description

**OGAI-8x7B-8bit-32k** is an **8-bit quantized version** of the OGAI-8x7B model with a **32K token context window**. This quantized model retains most of the capabilities of the original model while **significantly reducing memory requirements**, making it ideal for deployment in environments with memory constraints.

The model is based on a LoRA fine-tuned Mixtral-8x7B model, specifically engineered for oil and gas applications with a focus on drilling processes. The quantization to 8-bit precision offers a balanced approach between model size reduction and maintaining high-quality outputs for domain-specific tasks.

- **Developed by:** GainEnergy AI Team
- **Model type:** 8-bit Quantized Causal Language Model (Instruction Following)
- **Language:** English
- **License:** MIT
- **Finetuned from model:** [GainEnergy/ogai-8x7b](https://huggingface.co/GainEnergy/ogai-8x7b)
- **Quantization method:** 8-bit (Int8)
- **Context length:** 32,768 tokens

## Quantization Details

This model was quantized from the full-precision OGAI-8x7B using 8-bit quantization with the following configuration:

```python
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_enable_fp32_cpu_offload=True
)
```

The 8-bit quantization reduces the model size by approximately 4x compared to FP16, while preserving approximately 95-98% of the original model's performance on oil and gas engineering tasks.

## Key Capabilities

- **Drilling Calculations & Optimization**: Computes complex well trajectories, mud weight calculations, hydraulics, and casing designs.
- **Engineering Knowledge Integration**: Retains knowledge from oil & gas technical literature, drilling reports, and proprietary engineering datasets.
- **Intelligent Document Processing**: Supports knowledge retrieval for drilling workflows, regulatory compliance, and field operation manuals.
- **High-Context Reasoning**: The extended 32K token context window allows the model to retain context across long drilling plans, technical discussions, and simulation outputs.

## Usage

### Basic Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
import torch

# Configure quantization
quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_enable_fp32_cpu_offload=True
)

# Load tokenizer and model
model_id = "GainEnergy/ogai-8x7b-8bit-32k"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    quantization_config=quantization_config
)

# Example prompt for drilling engineering
prompt = "Calculate the required casing depth for a well with a pore pressure of 12.5 ppg."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Utilizing the Extended Context Window

To make the most of the 32K context window, you can input longer documents for analysis:

```python
# Load long document (e.g., drilling report, technical specifications)
with open("long_drilling_report.txt", "r") as f:
    long_document = f.read()

# Append a question at the end
prompt = f"{long_document}\n\nBased on the above document, what are the key risk factors identified for this drilling operation?"

# Process with appropriate truncation to fit within context
inputs = tokenizer(
    prompt, 
    return_tensors="pt",
    truncation=True,
    max_length=32000  # Leave room for generation
).to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=768,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
```

## Hardware Requirements

Due to the quantization, this model requires less GPU memory than the full-precision version:

- **Minimum:** CUDA-capable GPU with 16GB VRAM
- **Recommended:** CUDA-capable GPU with 24GB+ VRAM for comfortable usage with the 32K context window
- **System RAM:** 32GB+

## Limitations

- **Performance tradeoff:** While 8-bit quantization preserves most capabilities, there may be slight reductions in accuracy for complex numerical computations compared to the full-precision model.
- **Domain specificity:** The model is focused on oil and gas drilling engineering and may not perform well for other domains.
- **Expert validation:** Outputs should be validated by domain experts before application in real-world engineering scenarios.
- **Knowledge cutoff:** The model's knowledge is limited to data available up to 2025.

## Comparison with Other Variants

| **Model Variant** | **Precision** | **Context Length** | **Memory Requirements** | **Performance Retention** | **Ideal Use Case** |
|-------------------|---------------|--------------------|-----------------------|--------------------------|-------------------|
| OGAI-8x7B         | Full (16-bit) | 32K                | 64GB+ VRAM           | 100% (Baseline)          | High-precision engineering calculations |
| OGAI-8x7B-8bit-32k | 8-bit         | 32K               | 16-24GB VRAM         | ~95-98%                  | Balanced approach for deployment |
| OGAI-8x7B-4bit    | 4-bit (NF4)   | 32K                | 8-16GB VRAM          | ~90-95%                  | Highly constrained environments |

## Citation

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

## Acknowledgments

This model builds upon the work of the [OGAI-8x7B](https://huggingface.co/GainEnergy/ogai-8x7b) base model and extends its capabilities through quantization and context length expansion. Special thanks to the Mixtral team for the base architecture that powers this model.