One click tool for LLMs
Revolutionize your approach to fine-tuning large language models (LLMs) with our all-in-one platform. Effortlessly train and deploy models with a single click. No structured data? No problem! Our built-in data pipeline seamlessly transforms unstructured datasets into structured, trainable formats—enabling businesses to focus on innovation while we handle the complexity.
- Fine-tuning large language models (LLMs) is complex and resource-intensive.
- Traditional fine-tuning requires structured data, limiting flexibility for businesses with unstructured datasets.
- Businesses often lack the infrastructure and expertise to fine-tune models effectively at scale.
- The fine-tuning process is time-consuming, slowing down AI adoption and deployment.
- Unstructured data, which constitutes a majority of real-world data, is underutilized in model training.
This creates a barrier for businesses needing customized AI solutions but lacking the necessary resources to make the most of LLMs.
-
Setup Docker
-
Build Docker image
docker build -t cuda_pytorch_docker .- Run Docker container
Docker run --gpus all -it --rm -v <your folder path>:/OCT4LLM cuda_pytorch_docker- Boost Efficiency & Cut Costs: Simplify the fine-tuning process and reduce infrastructure needs.
- Unlock Data Potential: Leverage both structured and unstructured data for smarter insights.
- Scale & Innovate: Quickly deploy customized AI models across multiple use cases, gaining a competitive edge.
- SMEs: Easily fine-tune powerful AI models without heavy infrastructure or technical expertise.
- Data-Intensive Industries: Maximize unstructured data for smarter decision-making, especially in sectors like healthcare and e-commerce.
- AI Developers & Startups: Simplify model training and deployment for faster innovation.
- Enterprises Scaling AI: Efficiently deploy customizable AI models across various applications.
- Business Chatbots: Fine-tune models to create intelligent, domain-specific chatbots for customer support and engagement.
- One-Click Classifier Models: Deploy sentiment analysis, content categorization, and other classification models in minutes.
- Model Quantization Variability: Fine-tuning different architectures requires optimized quantization strategies.
- Inference and Training Optimization: Reducing computational overhead without sacrificing accuracy.
- Unstructured Data Processing: Transforming unstructured data into structured formats for efficient model training.
- Knowledge Distillation: Transfer knowledge from larger models to smaller ones for improved performance.
- Cloud-Based Deployment: Enable scalable, cloud-based training and deployment for easier access.
- User Interface Enhancements: Continuously improve the platform’s UI/UX for better user experience.
- Retrieval-Augmented Generation (RAG) Integration: Expand capabilities to enhance data retrieval and knowledge augmentation in future updates.
- The hosted link will be available for the next 72 hours after submission. After that, the link will no longer be active.
- We will update the GitHub repository with a new link for the next 10 days, but beyond that, we cannot continue hosting due to high computational costs.
- For participants: Please run the application locally using Docker to avoid overloading the hosted server.
- For judges: You can access the application via the provided URL during the review period.
Note: Each model training process may take several hours, so patience is key as you wait for the models to be ready for download or deployment.