Compare Open-Source LLM Popularity

The Hugging Face Models Scraper helps AI platform teams, analysts, and technical buyers compare public LLM attention signals. It collects public records into a bounded Apify dataset while preserving the query or category that produced each result. That provenance matters when a spreadsheet becomes a recurring workflow rather than a one-off browse.

This guide uses a deliberately narrow starting point: text-generation models matching llama. Small inputs make field coverage, duplicate behavior, source changes, and cost visible. A large first run can hide all four.

Compare attention without calling it quality

A useful LLM comparison table groups related artifacts before ranking them. Base models, instruction variants, community fine-tunes, and quantized files should not occupy identical rows in a buyer-facing chart. Preserve every model ID, then add a reviewed family and artifact-type mapping. Report downloads and likes as separate counters with the collection date.

Popularity can help allocate scarce evaluation time. It cannot replace evaluation. Choose a small set from different publishers and licenses, then run the same prompt suite, latency setup, hardware, and safety checks. Record the Hub snapshot that generated the shortlist. If a model later gains downloads or changes files, the original decision remains explainable because the source snapshot and evaluation results were not blended into a mutable score.

Define the question before collecting data

Write down the decision the dataset is meant to support. Name the records that qualify, the freshness window, the minimum fields required, and who reviews exceptions. For this workflow, downloads and likes should be examined alongside stable identifiers and current source URLs. Avoid a score that quietly combines unrelated signals.

Set a maximum result count that is cheap to inspect by hand. Ten to fifty records is usually enough for the first pass. Open several ordinary rows, at least one sparse row, and one surprising result. If those examples do not support the intended question, adjust the input before scheduling anything.

Run a bounded Apify Task

Use the Actor input form to encode text-generation models matching llama. Keep each saved Task focused on one question, geography, topic, category, or counterparty set. Focused Tasks are easier to name, retry, audit, and retire.

After the run finishes, save the Actor build number, run ID, dataset ID, input, and collection time with the export. The Actor returns model ID, author, pipeline task, library, downloads, likes, license, tags, files, gated state, and update timestamps. Preserve raw values. Put classifications, scores, and business rules in a separate reviewed transformation so source evidence is never overwritten.

Check the evidence at the source

Use popularity to prioritize evaluation, then run the same task-specific benchmark for every shortlisted model. Sample more records after a source-layout or API change. Check identifiers, canonical URLs, dates, numeric fields, arrays, and null rates. A result should be reproducible from its input and source link.

Downloads can include automated pulls, cached builds, and repeated usage; they are not unique users. That limitation belongs in the workflow documentation, not in a footnote added after someone questions the output. Missing fields should remain null rather than becoming zero, false, or an invented label.

Turn snapshots into reliable monitoring

Choose a cadence that matches the decision. Daily collection suits fast-moving operational queues. Weekly snapshots are often enough for market or catalog monitoring. Monthly runs can support slower benchmarks. Store the previous successful dataset and compare stable IDs plus named material fields.

Do not advance the baseline after a failed, partial, or unexpectedly empty run. Separate additions, updates, and removals. An empty dataset is an incident to investigate, not proof that the market disappeared. Alerts should include changed fields, collection time, the source URL, and a link to the Apify run.

Model the dataset without erasing history

Use the source identifier as the primary upsert key. Keep first-seen, last-seen, source-updated, and collected-at timestamps separate because they answer different questions. Retain the original text beside any normalized value. If entity resolution is needed, store the mapping with a confidence note and reviewer rather than silently merging names.

For trend analysis, compare like with like. The same queries, categories, page limits, sort order, and Actor version should be used across snapshots. If an input changes, begin a new series or annotate the break. Otherwise a collection change can be mistaken for a market change.

Export the result and control access

Apify datasets can be downloaded as JSON, CSV, or Excel or consumed through the API, webhooks, Make, Zapier, n8n, and MCP workflows. Keep credentials outside Actor inputs. Restrict downstream access to the use case and define retention for raw snapshots, derived tables, and alerts.

The Hugging Face Models Scraper on Apify charges per saved result and exposes an explicit result limit. Start with a small verified run. Scale only when the extra records change a real decision and the review process can absorb them.

Operational checklist

Before scheduling, confirm that the input is allowed, bounded, and documented. Verify representative output against the source. Record expected row count and acceptable null rates. Assign an owner for failed runs and source changes. Finally, write a stop condition: retire or revise the Task when its data no longer supports the original decision.

Compare families and variants at the right level

Decide whether the unit of analysis is a publisher, base model, fine-tune, quantization, or individual Hub repository. Summing every derivative into one family can double-count attention; comparing one quantized upload with a flagship repository can understate adoption. Publish both the grouping rule and an ungrouped appendix. Then place downloads and likes beside model age, task tag, update recency, and gated status. Popularity helps prioritize evaluation capacity, but benchmark quality, operating cost, license fit, and safety behavior remain separate questions.