CodeSoft/slm-arena-data
1.81 MB
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CodeSoft
AI & ML interests
Working on small language models
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updated a bucket about 18 hours ago
CodeSoft/slm-arena-data updated a collection 1 day ago
Best medium-sized models (8 to 30B) updated a collection 1 day ago
Best medium-sized models (8 to 30B)Organizations
updated a
bucket about 18 hours ago
updated a
collection 1 day ago
liked a
dataset 1 day ago
If they give some kind of GPU time for training, I would be so happy (although it would probably be pro only)
liked a
model 1 day ago
CodeSoft SLM Arena
โ 1
3
#1 opened 1 day ago
by 
wayneworkman2012
Open the user inputs
โ 1
2
#9 opened 1 day ago
by wayneworkman2012
liked a
Space 2 days ago
reacted to SeaWolf-AI's post with ๐ฅ 2 days ago
Post
3990
Introducing the Global LLM Download Leaderboard ๐
Cumulative download counts are a museum. They reward age, not relevance โ 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.
๐ 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 โ 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 ๐บ๐ธ USA ยท ๐จ๐ณ China ยท ๐ช๐บ 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 / ํ๊ตญ์ด 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 โ 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 โ 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 โ 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.
๐ 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 โ 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 ๐บ๐ธ USA ยท ๐จ๐ณ China ยท ๐ช๐บ 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 / ํ๊ตญ์ด 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 โ 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 โ 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
reacted to KlondikeDev's post with ๐ฅ 2 days ago
Post
2355
Boris-1.7-D60M-n30M out NOW!
opencerebral/Boris-1.7-D60M-n30M
Following this will be Boris-1.8-D60M-n30M, which will test both the n-gram embeddings AND a new architecture.
Then, Boris-2 will begin training!
opencerebral/Boris-1.7-D60M-n30M
Following this will be Boris-1.8-D60M-n30M, which will test both the n-gram embeddings AND a new architecture.
Then, Boris-2 will begin training!
Can you add SupraLabs models please?
2
#8 opened 3 days ago
by 
LH-Tech-AI
updated a
Space 2 days ago
replied to their post 3 days ago
Would you allow me to duplicate your SLM Arena space if i make changes to it? I see its under MIT but dont wanna end up like Multivex if you didnt mean to set it to MIT?
No, I intended for it to be MIT. You can absolutely duplicate it, just please have a visible attribution.
replied to their post 3 days ago
Accepted
replied to their post 3 days ago
Okay, but the invite says it expired. Could you please resend it?
replied to their post 3 days ago
Thanks for reaching out! Since I'm not part of the BananaMind project, could you clarify what kind of feedback you're looking for?
posted an update 3 days ago
Post
2635
Sorbet Mini Experimental is out!
It's not the best model in the 5M parameter range, but it's going to be a really useful model to train on top of. This release was mostly to prove that the model actually works. I trained it on 150M tokens from TinyStories in about 12 minutes.
Does anyone have any tips on training models in this size range? I want to make the full Sorbet Mini release as good as it can be.
It's not the best model in the 5M parameter range, but it's going to be a really useful model to train on top of. This release was mostly to prove that the model actually works. I trained it on 150M tokens from TinyStories in about 12 minutes.
Does anyone have any tips on training models in this size range? I want to make the full Sorbet Mini release as good as it can be.
- 13 replies
published a
model 3 days ago