What are Open-Source Models: Get Started with Open-Source Models on Hyperstack

Adopting open source across every industry is no longer an “if”, but a “what, when and how”. Red Hat's annual report, "The State of Enterprise Open Source," provides valuable insights into how companies employ open-source models to gain a competitive edge. The report is based on interviews with 1,296 IT leaders across 14 countries, offering an unbiased perspective on open-source adoption. But why open-source models? And how can you get started with these to stay ahead in the market? Continue reading to know how.

What are Open-Source Models?

Open-source models are pre-trained artificial intelligence (AI) models freely available under open-source licenses. They are designed to be widely accessible, allowing anyone to use, modify, and redistribute them. Unlike proprietary models, which often come with licensing fees and restrictions, open-source models empower organisations and developers with the flexibility to customise and optimise them for specific use cases.

Open-source models are available in two versions: foundation models trained on extensive datasets and leverage significant computational resources to provide general knowledge and capabilities and fine-tuned models that can be adapted for specialised tasks such as natural language processing, image recognition and machine translation.

Why Deploy Open-Source Models?

Here are some major reasons why you should deploy open-source models:

1. Cost Efficiency: Training large-scale AI models from scratch demands significant financial and computational resources. Open-source models eliminate this barrier, enabling businesses to save costs using pre-trained models as starting points. This is particularly helpful for startups or organisations with limited budgets.
2. Faster Development Cycles: By leveraging open-source models, developers can focus on fine-tuning and customisation instead of building models from the ground up. This dramatically reduces development timelines for businesses to bring AI-powered solutions to market faster.
3. Flexibility and Transparency: Open-source models are completely transparent regarding architecture, training procedures and datasets. This allows developers to inspect and optimise the models to meet specific requirements. 
4. Access to Cutting-Edge AI: Many open-source models represent the latest advancements in AI research. By using these models, businesses can stay at the front of innovation.
5. Community Support and Collaboration: The open-source community supports collaboration with global contributors constantly improving and refining the models. 

Getting Started with Open-Source Models on Hyperstack

Hyperstack is dedicated to supporting open-source models. Our platform is designed to meet those seeking efficient and cost-effective AI solutions. Here’s how to deploy open-source models on Hyperstack:

1. Choose the Right Open-Source Model

Selecting the right model is the first step. Platforms like Hugging Face offer a wide range of pre-trained models for tasks such as natural language processing and computer vision. For example:

• Llama 3.1:  Ideal for generating human-like text, conversational AI and fine-tuning for specialised NLP tasks.

• Stable Diffusion:  Ideal for generating images from text prompts.

• Mistral NeMo: Ideal for developing customised speech and language AI applications

• SAM 2: Ideal for advanced image segmentation, object detection and computer vision tasks.

• Flux.1: Ideal for generating high-quality images quickly based on detailed user prompts.

We also support popular frameworks like TensorFlow, PyTorch and Hugging Face to ensure seamless integration with your existing workflows.

Need help in selecting the right GPU for your LLM workload?

Try our LLM GPU Selector Tool. Simply choose your preferred model or a Hugging Face option and get personalised GPU recommendations for your project. Get started today.

2. Deploy Models Seamlessly on Hyperstack

Hyperstack makes deploying open-source models effortless by offering a highly optimised cloud environment, including:

• GPU Support: You can access high-performance NVIDIA GPUs such as the NVIDIA A100 and NVIDIA H100 PCIe designed to handle large-scale models effortlessly.
• Scalable Infrastructure: You can easily scale your workloads up or down to meet changing demands.
• One-Click Deployment: You can simplify setup with a one-click deployment option.

3. Fine-Tune for Your Use Case

Most open-source models are built for general-purpose tasks but you can fine-tune them to adapt to specific applications. On Hyperstack, you can easily and efficiently customise your models by:

1. Get started without delay: Our pre-configured images combined with NVIDIA drivers allow you to fine-tune tasks without wasting time on setup.
2. Experience unmatched speed: Get instant access to high-performance GPUs like NVIDIA A100 or NVIDIA H100 to accelerate your fine-tuning process and achieve faster results.
3. Customise with ease: Run advanced fine-tuning jobs like PyTorch LoRa effortlessly on any of our virtual machines, optimised for your needs.

4. Monitor and Optimise Performance

Running large-scale open-source models can be resource-intensive. Hyperstack provides features to help you monitor and optimise workloads while managing costs:

• Real-Time Cost Monitoring Tools: Our new summary section in the Billing Overview tab offers a clear snapshot of your monthly usage costs, virtual machines, volumes and other resources. With the new "View Details" button, you can easily see the Resource Activity tab for detailed insights. Learn more here.
• Cost-Effective Pricing: You can rent NVIDIA GPUs starting at $1.35/hr or reserve them long-term for as low as $0.95/hr. 
• Hibernation Options: You can easily save costs by pausing workloads when they’re not in use. Whether between training sessions or waiting for data inputs, our hibernation feature allows you to pick up exactly where you left off without paying for idle compute time.

5. Learn with Comprehensive Tutorials 

If you're starting with open-source models, Hyperstack provides comprehensive tutorials to support you at every stage. We regularly release tutorials to stay current with the latest open source LLM models.

Explore our tutorials below to begin your journey:

• Deploying and Using Pixtral Large Instruct 2411
• Deploying and Using Qwen 2.5 Coder 32B Instruct
• Deploying and Using Stable Diffusion 3.5
• Deploying and Using Notebook Llama
• Deploying and Using Granite 3.0 8B
• Deploying and Using Llama-3.1 Nemotron 70B 
• Deploying and Using Llama 3.2 11B 
• Deploying and Using Qwen2-72B 

Conclusion

At Hyperstack, we are all about supporting open source. By adopting open-source principles, we aim to provide developers, researchers and enterprises with the tools to drive impactful AI projects without being locked into proprietary ecosystems.

But wait, there’s more. Did you know we’ve open-sourced our Hyperstack LLM Inference Toolkit?

Our latest toolkit enables fast and efficient deployment and management of LLMs on Hyperstack for automation and streamlined workflows. It is an open-source Python package with a user-friendly UI, API and extensive documentation. We’re excited to share this resource with the developer community. Check it out on GitHub now: Hyperstack LLM Inference Toolkit.

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FAQs

What is open source?

Open-source AI models refer to AI models that are freely available for anyone to use, modify and distribute, encouraging collaboration and transparency.

What GPUs are available on Hyperstack for running open-source models?

Hyperstack offers NVIDIA A100 and H100 GPUs for high-performance workloads.

Can I integrate my existing workflows with Hyperstack?

Yes, Hyperstack supports popular frameworks like TensorFlow, PyTorch, and Hugging Face.

Where can I find resources to get started with open-source models?

You can find Hyperstack’s comprehensive tutorials to get started with open-source models: https://www.hyperstack.cloud/technical-resources/tutorials.