fromziro/Width-Vs-Depth
Updated • 2
🏗️ Building on HF
Dan P
Datdanboi25
AI & ML interests
Axiomic Labs Founder
Mechatronics engineer, LLMs, Vision Models, Embedding Models
Recent Activity
new activity about 3 hours ago
appvoid/void.0:Cool model! liked a model about 4 hours ago
fromziro/Width-Vs-Depth new activity about 4 hours ago
fromziro/Width-Vs-Depth:Update README.mdOrganizations
Cool model!
🔥 1
3
#1 opened 1 day ago
by Datdanboi25
liked a
model about 4 hours ago
Update README.md
#1 opened about 4 hours ago
by Datdanboi25
reacted to Enderchef's post with 🤗 about 6 hours ago
Post
3181
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
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
liked a
Space about 6 hours ago
dont forgot my models
Should submit them to the leaderboard, would help with publicity!
liked 3
models about 9 hours ago
Can you add SupraElegans-500k please?
5
#75 opened 1 day ago
by 
AxionLab-official
updated a
Space about 10 hours ago
reacted to KlondikeDev's post with 🔥 about 23 hours ago
Post
899
Boris-2 coming soon!
The Models:
Boris-2-75M: Trained on 26B tokens -- estimated to start training on August 12th.
Boris-2-125M: Trained on 90B tokens -- Estimated to start training on August 20th.
Boris-2-250M: Trained on 60B tokens -- Estimated to start training on September 10th.
Why does 125M get more tokens than 250M?
Well, the straight answer is time. It saves time, while still allowing the 250M model to exceed the 125M model.
Furthermore, we are attempting a unique architecture and layering scheme to hopefully end up around the strength of SmolLM2-135M. Fingers crossed!
We hope to end up in the ballpark of AxiomicLabs/GPT-X2.5-135M or BananaMind/BananaMind-2-Pro-Preview
Following this, we will release the Pro, Instruct and Pro-Instruct variants. More info will be coming soon!
The Models:
Boris-2-75M: Trained on 26B tokens -- estimated to start training on August 12th.
Boris-2-125M: Trained on 90B tokens -- Estimated to start training on August 20th.
Boris-2-250M: Trained on 60B tokens -- Estimated to start training on September 10th.
Why does 125M get more tokens than 250M?
Well, the straight answer is time. It saves time, while still allowing the 250M model to exceed the 125M model.
Furthermore, we are attempting a unique architecture and layering scheme to hopefully end up around the strength of SmolLM2-135M. Fingers crossed!
We hope to end up in the ballpark of AxiomicLabs/GPT-X2.5-135M or BananaMind/BananaMind-2-Pro-Preview
Following this, we will release the Pro, Instruct and Pro-Instruct variants. More info will be coming soon!
- 4 replies
liked a
model 1 day ago
A simple text generation model. Would be cool to build a coding model, but that’s a challenge in and of itself.
Sweet, will chuck you a follow, keen to see how they turn out!
reacted to wop's post with 🔥 1 day ago
Post
1877
🧪 SlopFinder is here!!
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)
Dataset:
bench-labs/slop-classification
@benchlabs
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?**
No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. 🧩
How does it work?
Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.
Vote here:
https://bench-labs.web.app/slopfinder.html
(refresh page if you want to skip)
Dataset:
bench-labs/slop-classification
@benchlabs
- 17 replies
Yeah and I’m new making models so happy to find a community! I can learn a lot here!
Well welcome to the community! What type of models are you making?
Any news on Veyra-3?
2
#1 opened 1 day ago
by Datdanboi25
Well then IMO the slm community is a great place to be, you can learn so much by looking at other models, (especially since almost all of them are both open weights and open recipe!)