Manikanta21/OceanGPT-v1

Model Card for Model ID

Model Details

Model Description

• Developed by: Developed by Viswanadhapalli Manikanta, an aspiring AI/ML engineer focused on LLM fine-tuning, cloud technologies, and software development.
• Funded by : Personal learning and research initiative, self funded using the kaggle free GPU
• Shared by : Viswanadhapalli via HuggingFace Hub
• Model type: Parameter-Efficient Fine-Tuned (PEFT) Large Language Model using LoRA adapters.

Model Sources [optional]

• Repository: Repository: https://huggingface.co/Manikanta21/OceanGPT-v1

Uses

This model is intended for educational, research, and conversational AI applications. It is designed to assist users with instruction-following tasks, technical question answering, coding-related discussions, and general natural language understanding.

The model is suitable for:

• AI/ML experimentation
• Educational assistance
• Conversational applications
• NLP research and development
• Developer productivity workflows

Foreseeable users include:

• Students
• Developers
• Researchers
• AI enthusiasts
• Software engineers

How to Get Started with the Model

This model is designed for oceanographic and marine-related natural language processing tasks, including intent detection, marine data interpretation, and domain-specific conversational assistance.

1. Install Required Libraries

pip install transformers peft accelerate torch

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2. Load the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Base model
base_model_name = "OceanGPT-v1"

# LoRA adapter repository
adapter_model_name = "Manikanta21/OceanGPT-v1"

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(adapter_model_name)

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load PEFT adapter
model = PeftModel.from_pretrained(
    base_model,
    adapter_model_name
)

model.eval()

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3. Example Inference

prompt = "Explain the impact of ocean temperature changes on marine ecosystems."

inputs = tokenizer(
    prompt,
    return_tensors="pt"
).to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=150,
        temperature=0.7,
        do_sample=True
    )

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

print(response)

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Results

The fine-tuned model demonstrated improved understanding of oceanographic terminology and marine-domain conversational tasks compared to the untuned base model.

The model generated more contextually relevant and domain-aware responses for ocean science prompts and intent-based queries.

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Summary

The model successfully adapts a pretrained language model toward oceanographic NLP applications using PEFT/LoRA fine-tuning. It performs well for educational, conversational, and research-oriented marine-domain tasks while maintaining lightweight adapter-based deployment efficiency.

Technical Specifications

Hardware

• NVIDIA GPU provided through Kaggle environment
• CUDA-enabled acceleration
• GPU memory optimized using fp16 mixed precision training

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Software

• Python
• PyTorch
• Hugging Face Transformers
• PEFT
• Accelerate
• Hugging Face Hub

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Glossary

Glossary

• LLM (Large Language Model): A transformer-based AI model trained on large-scale text data for natural language understanding and generation.
• PEFT (Parameter-Efficient Fine-Tuning): A fine-tuning approach that updates only a small subset of model parameters instead of retraining the entire model.
• LoRA (Low-Rank Adaptation): A PEFT technique used to efficiently adapt pretrained language models with lower computational and memory requirements.
• Transformers: A deep learning architecture widely used in NLP tasks. This project uses the Hugging Face Transformers library for model loading, tokenization, and inference.
• Hugging Face Transformers: An open-source Python library that provides pretrained transformer models and tools for NLP, text generation, and fine-tuning workflows.
• BitsAndBytes: A library used for low-bit quantization and memory-efficient loading/training of large language models, commonly used for 4-bit and 8-bit optimization.
• Safetensors: A secure and efficient tensor storage format designed for safely saving and loading model weights without arbitrary code execution risks.
• .pt Files: PyTorch checkpoint files commonly used to store model weights, optimizer states, or training checkpoints.
• .pth Files: PyTorch serialized files used for saving model parameters, random states, or checkpoints during training.
• Training Arguments: Configuration parameters used during training such as learning rate, batch size, epochs, optimizer settings, and mixed precision settings.
• Tokenizer: A component that converts raw text into token IDs understandable by transformer models.
• NLP (Natural Language Processing): A field of AI focused on understanding and processing human language.
• Oceanographic Intent: The classification or understanding of marine and ocean-related user queries and instructions.
• Inference: The process of generating predictions or responses using a trained model.

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Model Card Authors

• Viswanadhapalli Manikanta

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Model Card Contact

• Hugging Face: https://huggingface.co/Manikanta21
• LinkedIn: https://www.linkedin.com/in/manikanta-viswanadhapalli-b32547311/

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Framework versions

• PEFT 0.19.1
• Transformers
• PyTorch
• Accelerate