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, orphi) 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?
phi, gemma, tinyllama Premium / High-Capacity Models: llama3:70b, codellama:34b, wizardlm:uncensored5
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,codellamaall 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 |