---
base_model: zjunlp/OceanGPT-basic-7B-v0.1
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:zjunlp/OceanGPT-basic-7B-v0.1
- lora
- peft
license: apache-2.0
---

# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->



- **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]

<!-- Provide the basic links for the model. -->

- **Repository:** Repository: https://huggingface.co/Manikanta21/OceanGPT-v1
- 
## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

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

```bash
pip install transformers peft accelerate torch
```

---

### 2. Load the Model

```python
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()
```

---

### 3. Example Inference

```python
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)
```

---

### 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.

---

#### 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

---

#### Software

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

---

## 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.

---

## Model Card Authors

- Viswanadhapalli Manikanta

---

## Model Card Contact

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

---

### Framework versions

- PEFT 0.19.1
- Transformers
- PyTorch
- Accelerate