Choosing A Model

When selecting an Ollama model for your specific needs, it’s important to consider a few key factors that will influence performance and suitability. Below are the steps to help you make the best choice

1. What are your objectives?

   Clearly outline what you aim to achieve with leveraging an LLM as a part of your backend. Consider the model’s application—whether it’s natural language processing, predictive analysis, or any other specific task.

   Ask yourself:

   Are you building a chatbot, summarizing content, analyzing sentiment, or extracting structured data?

   Examples & Recommendations:

   • Chatbot or general assistant: llama3, mistral, gemma

   • Content summarization or rewriting: llama2, phi, mistral

   • Code generation or technical Q&A: codellama, deepseek-coder

   • Specialized reasoning tasks: wizardlm, nous-hermes

2. What data will you be working with?

   Evaluate the types and quantity of data accessible for training and testing. Ensure the model you choose can work effectively with your data type and size. This is especially important if you plan to work with data other than plain text, such as images or video.

   Ask yourself:

   Will the model handle text, images, code, or a combination?

   Examples:

   • For text-only workflows, most Ollama models (like mistral, llama3, or phi) work well.

   • If you’re working with multimodal inputs (images, audio), consider an external pipeline—Ollama currently focuses on LLMs optimized for text.

3. Model Complexity

   • Simple Models: If your application requires quick results and you have less computational power, opt for simpler models. They’re easier to implement and require less processing time.

     • Use for fast, low-latency tasks on smaller infrastructure.

     • Examples: phi, tinyllama, gemma

   • Complex Models: For tasks demanding high accuracy and working with large-scale data, or different data types such as images, audio, or video, complex models are usually a better option.

     • Better for high-accuracy, large-context reasoning or specialized use cases.

     • Examples: llama3:70b, wizardlm, codellama:34b

4. Cost Analysis

   Analyze the budget you have against the cost of implementing and running the model. If you need assistance with this, reach out to your Xano representative.

   • Cost-Effective Models: Great for limited budgets but may sacrifice some accuracy or features.

   • Premium Models: Require a higher investment but provide better accuracy and features.

   Ask yourself:

   • Do I need real-time responses, or can I batch responses?

   • What’s my budget for GPU or CPU usage?

   Cost-Saving Models: phi, gemma, tinyllama Premium / High-Capacity Models: llama3:70b, codellama:34b, wizardlm:uncensored

5. Vendor / Community Support

   Select an Ollama model backed by strong community support or vendor assistance. This will aid in troubleshooting issues or optimizing performance.

   Recommended:

   • llama3, mistral, codellama all have strong GitHub and forum support.

   • Stick with models that are well-documented and frequently updated.

Use Case	Recommended Models
Lightweight Chatbot	phi, gemma, tinyllama
Developer Assistant	codellama, deepseek-coder
Content Generation	mistral, llama3, nous-hermes
Reasoning & Q&A	wizardlm, llama3:70b
Small Infra / Fast Load	phi, gemma
High Accuracy / Large Scale	llama3:70b, wizardlm, codellama:34b
Budget-Conscious Deployments	phi, gemma, tinyllama
Strong Community Support	mistral, llama3, codellama