Gemma 4
Collection
Gemma 4 is Google's new model family including including E2B, E4B, 26B-A4B, and 31B. • 43 items • Updated • 257
🌌 Literally Sedna
CNWPlayer
CNWPlayer
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upvoted a collection about 10 hours ago
Gemma 4 liked a model 2 days ago
deepseek-ai/DeepSeek-V4-Pro-0813 liked a model 3 days ago
Qwen/Qwen3.8-27BOrganizations
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upvoted a collection about 10 hours ago
liked a
model 2 days ago
liked a
model 3 days ago
27b when?
🚀 10
17
#1 opened 3 days ago
by 
tigerjjw53
liked a
model 3 days ago
reacted to Banaxi-Tech's post with 👍❤️🔥 3 days ago
Post
1869
We're excited to release BananaMind 2 SLMoE, an experimental sequence-level mixture-of-experts model.
It uses only 8M parameters per message but has 25M total parameters, 13 experts (out of 64) are selected based on the message prefix and reused for the entire response.
We're testing with this sequence-level architecture to find out how big the capability loss actually is and how much of it can be fixed.
The long-term idea is that this could make very large sparse models usable on machines that can't fit them in RAM by putting the entire model (which is big) on disk and only loading the active parts into VRAM.
This architecture is still in research and shouldn't be used for production models.
We trained it on 60B tokens (of FineWeb-HQ, FineWeb-Edu, DCLM ,Cosmopedia v2, FineMath and NPSet-2) on 8 RTX Pro 6000s.
Check it out at BananaMind/BananaMind-2-SLMoE
Follow us for future models:
BananaMind
@vovaRL
@Banaxi-Tech
@DedeProGames
BananaMind 2 Pro in a few days. You've been waiting 22 days for it.
It uses only 8M parameters per message but has 25M total parameters, 13 experts (out of 64) are selected based on the message prefix and reused for the entire response.
We're testing with this sequence-level architecture to find out how big the capability loss actually is and how much of it can be fixed.
The long-term idea is that this could make very large sparse models usable on machines that can't fit them in RAM by putting the entire model (which is big) on disk and only loading the active parts into VRAM.
This architecture is still in research and shouldn't be used for production models.
We trained it on 60B tokens (of FineWeb-HQ, FineWeb-Edu, DCLM ,Cosmopedia v2, FineMath and NPSet-2) on 8 RTX Pro 6000s.
Check it out at BananaMind/BananaMind-2-SLMoE
Follow us for future models:
@vovaRL
@Banaxi-Tech
@DedeProGames
BananaMind 2 Pro in a few days. You've been waiting 22 days for it.
- 3 replies
OMG
😔❤️ 22
13
#5 opened 3 days ago
by Banaxi-Tech
liked 2
models 3 days ago
Quants wrong?
5
#2 opened 4 days ago
by CNWPlayer
liked a
model 5 days ago
updated a
model 5 days ago
published a
model 5 days ago
liked a
model 5 days ago
reacted to ProCreations's post with 🔥 6 days ago
Post
1890
new day, new models! Introducing the Auto series, a series of classifiers for determining if agentic tool calls are safe to run or not to prevent any harmful actions from happening. They have high performance compared to other LLMs commonly used for this task with incredible speed and small memory footprints.
ProCreations/auto-1b (recommended generally, much higher accuracy)
ProCreations/auto-0.4b (faster but worse)
Comes with datasets as well (open source ftw)!
A GitHub repo with pi extensions etc will come soon with this model.
ProCreations/auto-1b (recommended generally, much higher accuracy)
ProCreations/auto-0.4b (faster but worse)
Comes with datasets as well (open source ftw)!
A GitHub repo with pi extensions etc will come soon with this model.
- 2 replies
Wonder if someone could build an open-weights GPTZero alternative with this...
reacted to wop's post with 🔥🤗 7 days ago
Post
2305
🧪 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