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Eren Ekşi

ereniko

15 2 19

AI & ML interests

Optimizing training like crazy. And seals, they're not AI-related, but whatever.

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updated a Space about 8 hours ago

IvmeLabs/README

updated a collection about 8 hours ago

Conversate-XL

updated a collection about 8 hours ago

Conversate-XL

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replied to Banaxi-Tech's post 3 days ago

Absolutely not you can check my own models Ivme-Conversate-S-v1-Base and S-v2-Instruct if you want I hate to do unrelated mentioning but

They literally do what you say

Low training tokens, different architecture (for v1), high quality data and it flopped so bad I didn't put v2 to leaderboards.

Second you said SmolLM2 uses curated data

Well

I guess what do we use

Take a wild guess it's FineWeb-Edu!

Why?

BECAUSE IT'S HUGGINGFACE

THAT'S THE POINT OF HUGGINGFACE

Also you said things about water usage I can tell you BananaMind's model is definitely more efficient in the amount of compute spent on what hardware.

reacted to Enderchef's post with ❤️ 13 days ago

Post

3294

GPT-X2.5-135M is finally released! 🚀
The new flagship from Axiomic Labs takes 3rd on the open SLM leaderboard trailing only the SmolLMs, check it out and follow us:
AxiomicLabs/GPT-X2.5-135M

2 replies

reacted to FlameF0X's post with ❤️ 13 days ago

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274

Hello, people of Hugging Face!

I recently released FlameF0X/TinyMoE-100m-2x8-retrained, a small Mixture of Experts language model trained on the Smollm-Corpus. Built on top of the Mixtral architecture, it’s fully compatible with 🤗 Transformers right out of the box!

The model can produce somewhat coherent text on its own, and for some reason, it generates even more coherent responses when given a ChatLM template.

I’m excited to see what you all come up with, and feel free to fine-tune it if you’d like. In the meantime, I’ll be working on developing the chat-trained version.

Demo: FlameF0X/TinyMoE-Playground
Collection: https://huggingface.co/collections/FlameF0X/tinymoe

reacted to HannesVonEssen's post with 🚀🔥🤗❤️ 13 days ago

Post

2327

🏅 HF Viewer podium

Our top most active users:

🥇#1 @Quazim0t0
🥈#2 @IvmeLabs
🥉#3 @GODELEV

If you are an HF creator and want to try entering the podium, feel free to visit the embed page to add your graph!

https://hfviewer.com/model-card-embed

2 replies

reacted to AxionLab-official's post with ❤️😔 about 2 months ago

Post

5165

⚠️ Community Notice

We would like to clarify that SupraLabs has no affiliation, partnership, or connection whatsoever with "SupraLarps" or its members.

Please avoid interacting with their organization, repositories, or Spaces under the assumption that they are associated with us.

We are currently aware of the situation and have already contacted the appropriate channels to address it.

Thank you to everyone who continues to support SupraLabs. ❤️

12 replies

replied to AxionLab-official's post about 2 months ago

Why is Supralabs getting a lot of drama every day recently? There are trolls everywhere its sad looking at it.

replied to AxionLab-official's post 2 months ago

I mean, like, we are talking about a 50M model here. That thing, already in full precision, can barely do basic math. On 2 bits, even a single question being right is impressive.

posted an update 3 months ago

Post

300

Something concerning is happening with Nvidia's GPU pricing and benchmark transparency.
Look at how other hardware generations work: a new iPhone costs roughly what the previous one did. Same with Samsung Galaxy phones, same with AMD Ryzen CPUs. Price increases are modest and the specs are straightforward to compare. Nvidia has broken from this pattern. Each new generation costs meaningfully more than the last, while the benchmarks get harder to interpret, not easier.
Look at what's happening with the numbers. BF16 and FP16 performance figures, the ones most relevant for real workloads, are increasingly buried or absent. Sparsity is baked into headline numbers without always being clearly flagged. The B200 is being benchmarked as an NVLink pair rather than a single card in some comparisons. Each of these is defensible in isolation, but together they make it genuinely hard to do apples-to-apples comparisons across generations.
The inference vs training tension is worth flagging too. Lower precision formats are good for inference but introduce real tradeoffs for training. As Nvidia pushes further into inference optimization, NVFP4 being the latest example, it raises a fair question about where training workloads go. NVFP4 training exists, but documentation and real-world results are thin enough that most teams aren't seriously evaluating it yet.
The "ROCm is immature" narrative is also getting a bit stale. I've personally run workloads on the MI300X and honestly it felt smoother than I expected, even compared to CUDA in some cases. And the price to performance ratio isn't even close. AMD isn't a consolation prize anymore.
At some point, labs and enterprises doing the actual buying will have enough alternatives like AMD MI300X, custom silicon, and inference-optimized ASICs to push back. Nvidia is standing on thin ice, and a few more steps like this might be enough to crack it.

posted an update 4 months ago

Post

432

I don't know why, but lately there's been a growing problem on HuggingFace: the platform is filled to the brim with datasets of reasoning traces from large AI models. I feel like someone should address this, but the dataset tab is just full of reasoning and output traces from models like Claude Opus and the model tab is full of fine-tunes trained on these.
What scares me most are the legal consequences of this, and the possibility that all models will start converging on the same tone because everyone is just fine-tuning on everyone else.

1 reply

replied to Fuwn's post 6 months ago

Opus 4.6 released

But idk this thing didnt really looked like a magic.

Though Sonnet 5 could also drop i dunno.

Eren Ekşi

AI & ML interests

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