LEADBOARD - ADMET, Kinase and Toxicity Prediction Benchmark
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Benchmark for drug prediction tools - ADMET, kinase, tox
🔄 In a Training Loop
VIDRAFT_LAB
SeaWolf-AI
AI & ML interests
Contact: arxivgpt@gmail.com
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reacted to their post with 🤗👍❤️👀 9 days ago
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4050
Introducing the Global LLM Download Leaderboard 🌍
Cumulative download counts are a museum. They reward age, not relevance — a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.
So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.
👉 VIDraft/global-llm-leaderboard
Why a 30-day window changes what you see
A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions — and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.
What it shows
Global Top 300, with tabs for 🇺🇸 USA · 🇨🇳 China · 🇪🇺 EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / 한국어 with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models — the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche — a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.
Benchmarks measure what a model can do. Downloads measure what people choose to use.
Cumulative download counts are a museum. They reward age, not relevance — a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.
So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.
👉 VIDraft/global-llm-leaderboard
Why a 30-day window changes what you see
A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions — and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.
What it shows
Global Top 300, with tabs for 🇺🇸 USA · 🇨🇳 China · 🇪🇺 EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / 한국어 with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models — the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche — a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.
Benchmarks measure what a model can do. Downloads measure what people choose to use.
- 5 replies
replied to their post 9 days ago
Fixed and live. params now derives from safetensors.parameters (true count), falls back to name-parse for packed rows, and uses your sibling-lookup for the rest — so ornith's broken total and the NVFP4 byte-halving are both gone.
With that correction, XS no longer leads: XS 30.1% vs S (3–15B) 31.2%. "Small is winning" collapses to a near-tie — exactly your point. Thank you for the rigor.
replied to their post 9 days ago
Thank you — excellent catch, and right on all three. Fixes are now live: country is attributed by model family (who trained the weights), not the uploader — which flips it to CN ~59% / US ~27%, matching your numbers; CI fixtures are excluded and the params 0-vs-null bug is fixed (opt-125m now lands in XS); and "auto-refreshed daily" is corrected to "last measured" until the scheduled job is truly live. Appreciate the rigor.
liked a
Space 9 days ago
reacted to their post with 🔥 9 days ago
Post
4050
Introducing the Global LLM Download Leaderboard 🌍
Cumulative download counts are a museum. They reward age, not relevance — a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.
So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.
👉 VIDraft/global-llm-leaderboard
Why a 30-day window changes what you see
A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions — and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.
What it shows
Global Top 300, with tabs for 🇺🇸 USA · 🇨🇳 China · 🇪🇺 EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / 한국어 with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models — the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche — a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.
Benchmarks measure what a model can do. Downloads measure what people choose to use.
Cumulative download counts are a museum. They reward age, not relevance — a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.
So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.
👉 VIDraft/global-llm-leaderboard
Why a 30-day window changes what you see
A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions — and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.
What it shows
Global Top 300, with tabs for 🇺🇸 USA · 🇨🇳 China · 🇪🇺 EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / 한국어 with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models — the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche — a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.
Benchmarks measure what a model can do. Downloads measure what people choose to use.
- 5 replies
posted an update 9 days ago
Post
4050
Introducing the Global LLM Download Leaderboard 🌍
Cumulative download counts are a museum. They reward age, not relevance — a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.
So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.
👉 VIDraft/global-llm-leaderboard
Why a 30-day window changes what you see
A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions — and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.
What it shows
Global Top 300, with tabs for 🇺🇸 USA · 🇨🇳 China · 🇪🇺 EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / 한국어 with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models — the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche — a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.
Benchmarks measure what a model can do. Downloads measure what people choose to use.
Cumulative download counts are a museum. They reward age, not relevance — a model released two years ago can sit near the top on the strength of downloads it earned long before anyone stopped using it. If you want to know what the open LLM ecosystem is actually running today, you need a different lens.
So we built one. The Global LLM Download Leaderboard ranks text-generation models by their trailing 30-day downloads, measured directly from the Hugging Face API and refreshed every day.
👉 VIDraft/global-llm-leaderboard
Why a 30-day window changes what you see
A cumulative chart answers "what has been popular." A 30-day chart answers "what is being adopted right now." Those are very different questions — and the second one is the one that matters if you're deciding what to build on, quantize, fine-tune, or serve this quarter. Momentum, not history.
What it shows
Global Top 300, with tabs for 🇺🇸 USA · 🇨🇳 China · 🇪🇺 EU
Six share-of-download charts: by country, by parameter size, by quantization, by type (Base / Instruct / Quantized / MoE), by release year, and by organization (Top 10)
Per-model chips for parameter size, quantization, license, and type
English / 한국어 with automatic browser-language detection and a manual toggle
What the data reveals
The frontier is bipolar. Two countries account for the large majority of the top-300's 30-day downloads. Open-model gravity is concentrating, not dispersing.
Small is winning. A striking share of all downloads goes to sub-3B models — the clearest signal yet that on-device and cost-efficient deployment, not maximum parameter count, is driving real-world adoption.
Quantization is mainstream. GGUF, AWQ, FP8 and friends aren't a niche — a large fraction of the most-downloaded artifacts are quantized, because that's what people actually run.
Benchmarks measure what a model can do. Downloads measure what people choose to use.
- 5 replies
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