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‎README.md‎

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<p align="center">
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<img width="75%" alt="mvlm logo" src="assets/logo2.svg" /><br>
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<b>Quickly find the <u>m</u>inimum <u>v</u>iable <u>l</u>anguage <u>m</u>odel (mlmv) for your task, for faster and cheaper intelligence</b>
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<img width="75%" alt="smollest logo" src="assets/logo2.svg" /><br>
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<b>Quickly find the <u>s</u>mollest <u>m</u>inimum <u>v</u>iable <u>l</u>anguage <u>m</u>odel for your task, for faster and cheaper intelligence</b>
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</p>
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The basic idea is to run your OpenAI/Anthropic API queries to other, smaller models on Hugging Face API (or local), allowing you to quickly find the smallest/cheapest/fastest model that would work for your use case.
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<p align="center">
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<img alt="mvlm dashboard screenshot" src="assets/screenshot.png" />
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<img alt="smollest dashboard screenshot" src="assets/screenshot.png" />
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</p>
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## Dashboard
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```bash
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mvlm show
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smollest show
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```
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Opens a web dashboard with projects in the sidebar, a results table with truncation for long outputs, latency and cost per model, and aggregate match rates. The image above shows the UI, which you can reproduce by cloning this repo and running: `python examples/demo_dashboard.py`

‎assets/logo.svg‎

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‎assets/logo2.svg‎

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‎mvlm/anthropic.py‎

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import anthropic as _anthropic
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except ImportError:
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raise ImportError(
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"anthropic package is required. Install it with: pip install mvlm[anthropic]"
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"anthropic package is required. Install it with: pip install smollest[anthropic]"
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)
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self._client = _anthropic.Anthropic(**kwargs)

‎mvlm/cli.py‎

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project = args[1] if len(args) > 1 else None
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report(project=project)
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else:
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print("Usage: mvlm [show|report] [project]")
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print("Usage: smollest [show|report] [project]")
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sys.exit(1)
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‎mvlm/openai.py‎

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import openai as _openai
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except ImportError:
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raise ImportError(
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"openai package is required. Install it with: pip install mvlm[openai]"
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"openai package is required. Install it with: pip install smollest[openai]"
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)
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self._client = _openai.OpenAI(**kwargs)

‎mvlm/package.json‎

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{
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"name": "mvlm",
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"name": "smollest",
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"version": "0.1.1",
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"description": "Minimum Viable Language Model",
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"description": "Smollest",
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"python": "true"
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}

‎mvlm/results.py‎

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candidate_latencies: dict[str, float],
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) -> None:
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print(f"\n{'=' * 60}")
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print(f"mvlm comparison — baseline: {baseline_model} ({baseline_latency_ms:.0f}ms)")
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print(f"smollest comparison — baseline: {baseline_model} ({baseline_latency_ms:.0f}ms)")
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print(f"{'=' * 60}")
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for comp in comparisons:
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candidates.setdefault(name, []).append(score)
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print(f"\n{'=' * 60}")
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print(f"mvlm summary — project: {proj} ({len(entries)} comparisons)")
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print(f"smollest summary — project: {proj} ({len(entries)} comparisons)")
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print(f"{'=' * 60}")
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for name, scores in sorted(candidates.items()):

‎mvlm/web.py‎

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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>mvlm — Minimum Viable Language Model</title>
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<title>smollest — Minimum Viable Language Model</title>
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<style>
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* { margin: 0; padding: 0; box-sizing: border-box; }
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svg.filters { position: absolute; width: 0; height: 0; }
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<body>
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<div class="sidebar">
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<h1>mvlm</h1>
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<h1>smollest</h1>
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<h2>Projects</h2>
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<div id="project-list"></div>
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</div>
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server = HTTPServer(("127.0.0.1", port), _make_handler(page))
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url = f"http://127.0.0.1:{port}"
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print(f"mvlm dashboard: {url}")
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print(f"smollest dashboard: {url}")
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print("Press Ctrl+C to stop")
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threading.Timer(0.5, lambda: webbrowser.open(url)).start()

‎pyproject.toml‎

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dynamic = ["version"]
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[project.scripts]
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smollest = "mvlm.cli:main"
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mvlm = "mvlm.cli:main"
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[project.urls]

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