AxionLab-official (AxionLab)

posted an update 6 days ago

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Please, give a follow to SupraLabs!

We are researching the most, just to make the best medium models FOR YOU!

SupraLabs/Supra-A2A-Nano-Exp

SupraLabs/Supra-1.5-50M-Instruct-exp

SupraLabs/Supra-50M-Reasoning

SupraLabs/supra-title-50M-pre-gguf

Check more at Supralabs org!

SupraLabs

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@LH-Tech-AI
@QyrouNnet-AI
@LyJonathan
@Mmorgan-ML
@User01110

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posted an update 9 days ago

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# An Open Letter from SupraLabs.

Over the past few days, SupraLabs has been mentioned in a public discussion regarding small language models, scaling laws, and training methodology. We'd like to clarify our position.

Before anything else, we want to make one thing absolutely clear: we have great respect for Lane and the work being done at Glint Research. At no point was our intention to disrespect Lane, Glint Research, or their research. What began as a technical discussion about model scaling and training methodology unfortunately became much more personal than we ever intended. From our perspective, it was simply an exchange of technical opinions, and we sincerely hope it remains that way.
We'd also like to acknowledge that one of our own comments during the discussion was poorly worded. Referring to a benchmark as "fake" was imprecise. What we intended to criticize was the comparison methodology, not the integrity of the evaluation itself. Comparing a merged checkpoint against a single checkpoint is, in our view, not an apples-to-apples comparison.

That said, this was never the core of the discussion.

Our disagreement was not about SLERP, model merging, or whether training a small model on massive amounts of data is an interesting research direction. We support experimentation and unconventional ideas.

The actual point of disagreement was much simpler.

The statement that a 1M parameter model trained on 1 trillion tokens will become a "100M killer" is, today, a prediction, not an experimental result.
Could it happen? Perhaps.
Would it be exciting if it did? Absolutely.

But until benchmark results, reproducible evaluations, and independent validation exist, we believe such statements should be presented as hypotheses rather than established conclusions.
Research advances by testing ideas, not by assuming their outcomes.

We sincerely wish Lane and everyone at Glint Research success in their experiments.

Thank you to everyone who read it.

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reacted to pedrodev2026's post with 👍 12 days ago

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O NanoMathDataset é um dataset de quase 4M linhas de contas de matemática básica, veja ele em: pedrodev2026/NanoMathDataset

replied to their post 21 days ago

It turns out that you can cherry pick results, post an image, and act like it generalizes

wdym lmao, i never said the model generalizes it, i just posted a response about it when i tried it, the model isn't perfect, but isn't cool that a 50M params model quantized in 2Bit answer you like that?

replied to their post 21 days ago

3 days on a single RTX 5060 Ti 16GB

posted an update 21 days ago

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THIS IS CRAZY! THE MODEL ON THE IMAGE(Supra-50M-Reasoning) answered correctly and its QUANTIZED IN 2BIT! THE RESPONSE IS CORRECT, IN A 15MB SIZE FILE!

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posted an update 24 days ago

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We're happy to announce that we released a Reasoning tuned version of Supra-50M!

SupraLabs/Supra-50M-Reasoning

reacted to FlameF0X's post with 🔥 25 days ago

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MiniMax-M3 coming soon.
https://github.com/MiniMax-AI/MiniMax-M3

reacted to their post with 🚀 about 1 month ago

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Someone ran Supra-50M-Instruct ON A 1GHZ 1999 CPU

https://www.reddit.com/r/LocalLLM/comments/1tm21ar/i_see_your_strix_halo_and_raise_you_a_vintage/

"As a fun experiment, I decided to try running the recently released Supra-50m on a 26-year-old machine I keep for retro Windows 9.X games. Although the model was somewhat silly and inconsistent, the performance wasn't bad, reaching around 1.3 tok/s with CPU inference alone.

Since this CPU doesn't have SSE2, I changed from llama.cpp to llama2.ce and asked Claude to write a custom tokenizer.

It's crazy to think that with the right file size of 200 MB, we could have experienced this magic back in 1999" - u/drone_stonks, r/localllm

posted an update about 1 month ago

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Someone ran Supra-50M-Instruct ON A 1GHZ 1999 CPU

https://www.reddit.com/r/LocalLLM/comments/1tm21ar/i_see_your_strix_halo_and_raise_you_a_vintage/

"As a fun experiment, I decided to try running the recently released Supra-50m on a 26-year-old machine I keep for retro Windows 9.X games. Although the model was somewhat silly and inconsistent, the performance wasn't bad, reaching around 1.3 tok/s with CPU inference alone.

Since this CPU doesn't have SSE2, I changed from llama.cpp to llama2.ce and asked Claude to write a custom tokenizer.

It's crazy to think that with the right file size of 200 MB, we could have experienced this magic back in 1999" - u/drone_stonks, r/localllm

posted an update about 1 month ago

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We RELEASED!

SupraLabs just released our 50M model!
Base, Instruct Weights are there, you can use!

You can check blog to more informations!(Writing blog yet!)

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reacted to Reubencf's post with 🔥 about 1 month ago

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I have improved my Portfolio please do check it out
Reubencf/Portfolio

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reacted to appvoid's post with 👀 about 1 month ago

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As promised

appvoid/palmer-005-nano

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reacted to danielhanchen's post with 🚀🔥 about 1 month ago

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We’re excited to announce that Unsloth has joined the PyTorch Ecosystem! 🔥🦥

Unsloth is an open-source project that makes training & running models more accurate and faster with less compute. Our mission is to make local AI accessible to everyone. Thanks to all of you for making this possible! 💕

Blog: https://unsloth.ai/blog/pytorch
GitHub: https://github.com/unslothai/unsloth

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reacted to danielhanchen's post with 🚀🔥 about 2 months ago

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We collaborated with NVIDIA to teach you how we made LLM training ~25% faster! 🚀

Learn how 3 optimizations help your home GPU train models faster:
1. Packed-sequence metadata caching
2. Double-buffered checkpoint reloads
3. Faster MoE routing

Guide: https://unsloth.ai/blog/nvidia-collab
GitHub: https://github.com/unslothai/unsloth

reacted to Crownelius's post with 🤯 about 2 months ago

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Day 4-6 [05/05/2026]
Howdy,

Is anybody else willing to put a second mortgage on their house, just to spend 40k USD in compute credits? Just me? k...

I got dreams, man. The datasets I could build with 40k would be insane.
Somebody called me a genius the other day, they'd be shocked to find out, that I would put my house on the line for 30 days of runpod usage.

What would you do with it?
I would turn arxiv into a dataset. Turn each arxiv paper into a QnA.
Or... maybe if I got 40k USD in credit's Id end up like those 16 lost scientists.

Food for thought.
Anyways, I think I'm going to make a post once a week.
In the meantime you can find me building small llm's in discord here:
https://discord.gg/4DdwS9D8x9

edit:
So to be clear, I will not actually do this. But think of it this way. If you could pivot an entire industry with 10-30k would you do it?

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reacted to Crownelius's post with 🚀 about 2 months ago

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Day 3 - 05/02/2026
Scamp ships, hits the wall. New plan...

Scamp came back from training today... Didn't go so well, I'm still unsure...

Fast benchmark, temperature 0.7, top_p 0.9:
- "Capital of France is" produced "covered by the Crown" (grammatical, factually wrong)
- "23 + 19 = ?" produced "23. Answer: 23. Answer: 23..." (loops, math broken)
- "def fibonacci(n):" produced a list of letters

It speaks English. It can't reason. At 8K vocab and 50M params, it was never going to.

Next build: 412M MoE-3E. Three experts (math, language, code), top-1 routing, random init, let specialization emerge from gradient signal alone. Tried seeded Branch-Train-MiX first then dropped it. Adds compute for no clear win when the router will find its own attractors anyway.

Big lesson today came from limit testing on A100 80GB. Surprise, every planned phase ran out of memory even on 80GB. Root cause: at vocab 262144 (Gemma 3 standard), the output logits dominate during forward and backward. Fix: Liger Kernel's fused cross-entropy. It streams the loss computation instead of materialising the full B by T by vocab tensor. Without it the build would not run.

Scamp proved the pipeline runs end-to-end on real hardware. The 412M run starts tomorrow. If routing balances naturally and math finally crystallises, ships as Crowfeather-412M-3E with GGUF in F16, Q8, Q5, and Q4.

So... the training may have produced a poet if I had done it better. But I didn't, so instead... we get a malformed robot named Scamp... This is progress.

-Shane

P.S Join discord for discussion: https://discord.gg/8ZscHNmJYE and
I post my finished stuff here: CompactAI-O

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reacted to Crownelius's post with 🔥 about 2 months ago

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[DAY TWO] PROJECT CROWFEATHER - 5/1/2026
Que sera, what will he be?

Step 47,500 of 100,000. Loss hovering around 2.76 on 6.2B tokens. Throughput steady at 87k per second on the A100. Not a GH200, but she gets it done.

Still haven't named him. Scamp has a rascally charm. Quentin sounds like he'd wear a bow tie and think hard before speaking. Taking votes.

Phase two is what's keeping me up. Datasets everywhere and I can't pick. I'm fusing Google and DeepSeek's ideas: Gemma 4's alternating sliding and global attention, DeepSeek V4's Muon optimizer and WSD scheduler, Gemma 2's logit soft cap, and PaLM's z-loss. Sounds like peanut butter on a hamburger, but the loss curve says it works.

Tribe_v2 has real potential but needs more scaffolding than a barn raising before I throw it in. One thing's certain though. This model's gonna be a thinker. Not a Wikipedia parrot. Something that chews before it answers.

Finally got a use for my less popular datasets too. Some Opus-4.5-Writing-Style for polish. A few rows of Human-Archtypes-25k to see what personality bubbles up. Could be a poet, could be a grump. Either beats a flimsy fine-tune.

The bank's after my credit card. Until then, full steam.

Next model gets graphs. I swear.

-Shane

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