| --- |
| 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 |
|
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| <!-- Provide a quick summary of what the model is/does. --> |
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| ## Model Details |
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| ### Model Description |
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| <!-- Provide a longer summary of what this model is. --> |
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| - **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. |
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| ### Model Sources [optional] |
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| <!-- Provide the basic links for the model. --> |
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| - **Repository:** Repository: https://huggingface.co/Manikanta21/OceanGPT-v1 |
| - |
| ## Uses |
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| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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| 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. |
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| The model is suitable for: |
| - AI/ML experimentation |
| - Educational assistance |
| - Conversational applications |
| - NLP research and development |
| - Developer productivity workflows |
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| Foreseeable users include: |
| - Students |
| - Developers |
| - Researchers |
| - AI enthusiasts |
| - Software engineers |
|
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| ## How to Get Started with the Model |
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| This model is designed for oceanographic and marine-related natural language processing tasks, including intent detection, marine data interpretation, and domain-specific conversational assistance. |
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| ### 1. Install Required Libraries |
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| ```bash |
| pip install transformers peft accelerate torch |
| ``` |
|
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| --- |
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| ### 2. Load the Model |
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| ```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() |
| ``` |
|
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| --- |
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| ### 3. Example Inference |
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| ```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 |
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| The fine-tuned model demonstrated improved understanding of oceanographic terminology and marine-domain conversational tasks compared to the untuned base model. |
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| The model generated more contextually relevant and domain-aware responses for ocean science prompts and intent-based queries. |
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| --- |
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| #### Summary |
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| 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. |
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| ## Technical Specifications |
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| #### Hardware |
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| - NVIDIA GPU provided through Kaggle environment |
| - CUDA-enabled acceleration |
| - GPU memory optimized using fp16 mixed precision training |
|
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| --- |
|
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| #### Software |
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| - Python |
| - PyTorch |
| - Hugging Face Transformers |
| - PEFT |
| - Accelerate |
| - Hugging Face Hub |
|
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| --- |
|
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| ## Glossary |
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| ## Glossary |
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| - **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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| --- |
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| ## Model Card Authors |
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| - Viswanadhapalli Manikanta |
|
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| --- |
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| ## Model Card Contact |
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| - Hugging Face: https://huggingface.co/Manikanta21 |
| - LinkedIn: https://www.linkedin.com/in/manikanta-viswanadhapalli-b32547311/ |
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| --- |
|
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| ### Framework versions |
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| - PEFT 0.19.1 |
| - Transformers |
| - PyTorch |
| - Accelerate |
