← 返回原文 ↗
Hacker News 熱門49

MicroLLM Lab – Try 7 tiny LLM's in the browser

· 15天前
跳到正文

正文

Generate max new
Next action:

Objective checks (regex / exact tokens), not writing quality. A 135M model is allowed to fail — that is the measurement. Pick models, then run. Estimate uses your last tok/s if we have one.

Run suite on active model Run suite on every loaded model Run sustained speed test (256 tokens)
🏎️ Highest Peak Speed — Fastest single test
⚡ Highest Sustained Speed — Continuous 256-tok decode
📊 Avg Sustained Speed — Across tested models
⏱️ Total Benchmark Score — Cumulative suite wall time

Runtime per test (ms)

Accuracy per test

Next action:

Speed (tokens/s, sustained decode, suite wall) and accuracy (pass rate on objective tests) from runs in this browser. Numbers stay on this machine. Charts use the latest suite per model.

🏎️ Highest Peak Speed — Fastest single test
⚡ Highest Sustained Speed — Continuous 256-tok decode
📊 Avg Sustained Speed — Across loaded models
⏱️ Total Benchmark Score — Total execution wall time

Accuracy (latest suite)

Speed (mean tok/s)

Suite wall (ms)

Clear saved comparison
🏆

Verified Benchmark Certificate & Social Share

Generate and download a verifiable performance certificate with your device hardware, peak and sustained tokens/second, and share your score.

Your Name or Handle
Device & Hardware Information
🎨 Generate Certificate
MicroLLM Lab Benchmark Certificate
⬇️ Download Certificate Image (.png) 📋 Copy Image 𝕏 Share on X 💼 Share on LinkedIn 🔗 Copy Text Summary

Next action:

Write a benchmark in JavaScript

The editor is eval()’d in this origin, then each check runs on the model’s decoded text.

Example: object Example: function Eval and run

Prompt for a larger LLM

Copy prompt

Function form


            


            
            

              Next action:
              
            

          
        
查看原文（Hacker News 熱門）