AI Text & Vision Model Playground
Test, benchmark, and compare open-weights AI models including Llama 3, Mistral, and Stable Diffusion with temperature, top-p, and token streaming controls.
Prompt Configuration
~38 Prompt TokensReady / Completed
Here is the optimized O(N) time and O(1) space iterative implementation:
def fibonacci(n: int) -> int:
'''Calculate the n-th Fibonacci number iteratively.
Time Complexity: O(N)
Space Complexity: O(1)
'''
if n < 0:
raise ValueError("Fibonacci is undefined for negative integers.")
if n <= 1:
return n
prev, curr = 0, 1
for _ in range(2, n + 1):
prev, curr = curr, prev + curr
return curr
# Verification benchmark
if __name__ == "__main__":
assert fibonacci(0) == 0
assert fibonacci(1) == 1
assert fibonacci(10) == 55
print("N=10 Fibonacci:", fibonacci(10))About this tool
How to use it
- Select an open-weights foundation model from the dropdown (Llama 3.1 70B, Mistral Large 2, CodeLlama 34B, Stable Diffusion 3.5).
- Customize system instructions and prompt context, or load one of the quick developer prompt templates.
- Adjust temperature (randomness), top-p nucleus sampling, and maximum new tokens using the interactive sliders.
- Click 'Generate Completion' to simulate model output with live token count, tokens per second, and latency telemetry.
Why it matters
Testing AI model behavior across varying hyperparameters like temperature and top-p is critical for prompt engineers and software developers before deploying LLM integrations into production apps. This playground offers a lightweight, zero-latency environment to test prompts.
When to use it
Use when drafting system prompts, benchmarking prompt token lengths, testing structured JSON entity extraction, or experimenting with code generation instructions.
Common questions
What does the temperature parameter do in LLM inference?
Temperature controls the randomness of token selection. A temperature near 0.0 makes the output deterministic and focused, whereas higher values (0.7 to 1.2) increase diversity and creative exploration.
What is Top-P (nucleus) sampling?
Top-P sampling restricts candidate tokens to the smallest subset whose cumulative probability reaches P (e.g. 0.90), dynamically pruning low-probability nonsense tokens while preserving diversity.
Can I use my own Hugging Face API key?
Yes. You can optionally paste your Hugging Face User Access Token into the API token field to query live inference endpoints directly from your browser session.