hf-model-inspector 1.2.1

A package and GitHub Action to generate quality reports on Hugging Face model stats and usage, addressing ambiguity in open model releases.

HF Model Inspector

A package and GitHub Action to generate quality reports on Hugging Face model stats and usage, addressing ambiguity in open model releases.

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Overview

hf_model_inspector is a Python package and GitHub Action designed to provide clear, structured reports for models hosted on Hugging Face. Open model releases often come with incomplete or inconsistent metadata, making it hard to quickly assess model size, architecture, quantization, and usage statistics.

This tool helps you:

• Inspect model metadata including architecture, parameters, and downloads.
• Handle quantization info cleanly, even when formats differ across releases.
• Generate JSON and Markdown reports for documentation or review purposes.
• Recommend suitable models for your GPU based on memory constraints.
• Automate reporting with a GitHub Action for CI/CD pipelines.

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Installation

pip install hf_model_inspector

Optional: For private models, you can use a Hugging Face token.

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Quick Start

Example 1: Inspect a model and print summary

from hf_model_inspector import get_model_report_json

repo_id = "openai/gpt-oss-20b"
report = get_model_report_json(repo_id)

total_params = report["parameters"]["total"]
param_str = f"{total_params:,}" if total_params else "Unknown"

quant_info = report.get("quantization", {})
if quant_info.get("quantized"):
    methods = ", ".join(quant_info.get("quant_methods", [])) or "Unknown"
    precision = quant_info.get("precision", "Unknown")
    quant_status = f"{methods} ({precision})"
else:
    quant_status = quant_info.get("dtype", "Not Quantized") or "Not Quantized"

print(f"Model: {report['repo_id']}")
print(f"Architecture: {report['architecture']}")
print(f"Parameters: {param_str}")
print(f"Quantization: {quant_status}")
print(f"Downloads: {report['metadata']['downloads']}")
print(f"Tags: {', '.join(report['metadata']['tags']) if report['metadata']['tags'] else 'None'}")

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Example 2: Full inspection and Markdown report

from hf_model_inspector import get_model_report_md, save_model_report
from hf_model_inspector.loader import authenticate_hf

token = authenticate_hf()
repo_id = "openai/gpt-oss-20b"

# Generate and save Markdown report
report_md = get_model_report_md(repo_id, token)
save_model_report(repo_id, md_path="model_report.md", token=token)

print("Markdown report saved as 'model_report.md'")

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Features

• ✅ Inspect public and private models.
• ✅ Clean handling of quantization and parameter counts.
• ✅ Save JSON or Markdown reports.
• ✅ Recommend models suitable for your GPU.
• ✅ Automate with GitHub Actions for reproducible reporting.

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