Hugging Face's logo
Loom's picture
πŸ”„ In a Training Loop

Loom

textilelabs
3 1 5
Hillside502's profile picture grandcodepope's profile picture Stevve2's profile picture
Β·

AI & ML interests

Independent builder training small/efficient models from scratch on consumer hardware (CPU-only, no cloud).

Recent Activity

reacted to SeaWolf-AI's post with πŸ”₯ about 1 hour ago
Introducing the Global LLM Download Leaderboard 🌍 Cumulative download counts are a museum. They reward age, not relevance β€” 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. πŸ‘‰ https://huggingface.co/spaces/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 β€” 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 πŸ‡ΊπŸ‡Έ USA Β· πŸ‡¨πŸ‡³ China Β· πŸ‡ͺπŸ‡Ί 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 / ν•œκ΅­μ–΄ 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 β€” 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 β€” 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.
updated a model about 1 hour ago
textilelabs/Loom-Spark-2
published a model about 2 hours ago
textilelabs/Loom-Spark-2
View all activity

Organizations

None yet