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Models Playground & Fine-Tuning Inference Configuration

The Models Playground in Nexastack allows you to experiment with AI models, fine-tune inference parameters, and analyze how adjustments impact the model’s behavior — before deploying it into production.

This environment helps you find the ideal configuration that balances accuracy, creativity, and control for your specific use case.

Goal​

To explore different AI models, run example prompts, and adjust inference settings such as Temperature, Top-p, and Max Tokens to understand how each parameter influences model outputs.

Exploring the Models Playground​

Follow these steps to test and optimize model configurations in Nexastack.

Step 1: Access the Marketplace​

  1. Navigate to the Marketplace section in Nexastack.
  2. Browse through available AI models from the catalog.
  3. Select a model you want to explore.

Marketplace Overview

Step 2: Select a Model​

  1. Click on your desired model (for example, Qwen-32B) to open it in the Models Playground.
  2. You’ll be directed to an interactive interface where you can test prompts and modify settings.

Select Model Example

Step 3: Run a Prompt​

  1. In the text area, enter a sample prompt to evaluate the model’s response.
    Example Prompt:
  2. Click Run Prompt.
  3. Observe how the model responds to your input.

Run Prompt

Step 4: Adjust Inference Parameters​

Nexastack provides flexible controls for adjusting inference behavior.
You can tweak parameters like Temperature, Top-p, and Max Tokens to see how the model output varies.

[temp=0.1, top_p=0.9, token_limit=500]

We use Low Temp: to get the Simple code using for loops

Inference Controls

⚙️ Inference Parameters Explained​

Each inference parameter plays a critical role in shaping the behavior, tone, and consistency of model responses.

🔥 Temperature​

Purpose: Controls the creativity and randomness of generated text.

RangeTypical ValuesEffect
0 – 20.2, 0.5, 0.8Higher values make outputs more creative and diverse.

Behavioral Guide:

  • Low (0–0.3): Produces more deterministic and factual responses.
  • Medium (0.4–0.7): Balances between stability and creativity.
  • High (0.8–1.0+): Generates more imaginative and varied responses.

Examples:

TemperatureExample Output
0.2“Nexastack simplifies AI model deployment.”
0.8“Nexastack acts as your intelligent control center, enabling seamless deployment of scalable AI models.”
Recommendation

Use 0.3–0.7 for production workloads. Higher values are best for brainstorming or creative tasks.

🎯 Top-p (Nucleus Sampling)​

Purpose: Defines how diverse the output can be by controlling the probability distribution of possible words.

RangeTypical ValuesEffect
0 – 10.3, 0.8, 0.9Lower values restrict word choices; higher values increase diversity.

Behavioral Guide:

  • Low (0.1–0.3): Very focused and conservative responses.
  • High (0.8–1.0): Rich and creative outputs with more variability.

Example:

  • top_p = 0.3 → “Nexastack is a platform for deploying AI models.”
  • top_p = 0.9 → “Nexastack is an intelligent orchestration platform that empowers teams to deploy and scale AI solutions effortlessly.”
Tip

Top-p and Temperature work together. Increasing one may require lowering the other to maintain output coherence.

🧩 Max Tokens (Response Length)​

Purpose: Defines the maximum number of tokens (words or parts of words) the model can generate in its response.

RangeTypical ValuesEffect
1 – 4000+100, 250, 500Controls response length; higher values allow longer outputs.

Behavioral Guide:

  • Low (50–100): Short and concise responses.
  • Medium (150–300): Balanced explanations or summaries.
  • High (400–800): Long-form responses, narratives, or code outputs.

Example:

Max TokensExample Output
100“Nexastack helps deploy models quickly.”
400“Nexastack provides an integrated ecosystem for deploying, scaling, and managing AI models across distributed Kubernetes clusters with complete observability.”
Important

Setting very high max_tokens increases latency and cost. Choose a reasonable limit based on your use case.

How Parameters Work Together​

ParameterControlsRecommended RangeIdeal For
TemperatureCreativity & randomness0.3–0.7Balanced, stable outputs
Top-pToken diversity0.8–1.0Natural, human-like responses
Max TokensOutput length100–500Summaries or detailed explanations

Experimentation Workflow​

To get the most out of the Models Playground:

  1. Run sample prompts with different parameter combinations.
  2. Observe how the model’s tone, detail, and structure change.
  3. Identify the configuration that best fits your use case.
  4. Save the optimal setup for use in Model Deployment.
Pro Tip

The Playground helps you discover the best inference settings before deployment — minimizing trial and error in production.

Best Practices​

  • Start with balanced settings:
    Temperature: 0.5 | Top-p: 0.9
  • Increase temperature gradually for more creativity.
  • Set max_tokens according to your expected response length.
  • Keep notes of tested configurations for consistency.
  • Revisit the Playground regularly as models evolve.
Playground Complete

You’ve successfully explored, fine-tuned, and optimized inference configurations in the Nexastack Models Playground.
Your next step is to confidently deploy and monitor models in production.