Update: you can now also add custom conversations and score them!
♾️ Thinking
Wop
wop
AI & ML interests
AI research
AGI
Recent Activity
liked a model about 2 hours ago
TobiasLogic/chessmamba liked a model about 2 hours ago
wop/littlechat-50M repliedto their post about 3 hours ago
🧪 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:
https://huggingface.co/datasets/bench-labs/slop-classification
@benchlabs
Organizations
replied to their post about 3 hours ago
reacted to their post with 🤗 about 5 hours ago
Post
248
🧪 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
- 2 replies
posted an update about 5 hours ago
Post
248
🧪 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
- 2 replies
reacted to appvoid's post with 🔥 about 21 hours ago
Post
1782
If you want small models to be great again you should give a follow to people like @Banaxi-Tech or @Datdanboi25
These guys are rocking it with small models lately.
(They are not paying me to say that)
These guys are rocking it with small models lately.
(They are not paying me to say that)
- 10 replies
reacted to onekq's post with 👀 1 day ago
Post
2101
DeepSeek plans to raise token prices. I don't think this is because they are bleeding, but they are overwhelmed. If your price is 1/10 of your affiliate vendors, you can't leverage their resources. Markup is the only way to diverge traffic away.
Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.
This is where our attention should be (but distracted by things above).
Sadly I haven't found discussions on differentiators enabling DS to balance cost at such low prices. All software solutions (that we know of) are accessible by other vendors. If you attribute it to electricity or hardware, you can't explain why GLM and Kimi charge so much for their APIs.
This is where our attention should be (but distracted by things above).
- 5 replies
You should check out benchlabs, we have great text to image models
reacted to appvoid's post with 👀 1 day ago
Post
1782
If you want small models to be great again you should give a follow to people like @Banaxi-Tech or @Datdanboi25
These guys are rocking it with small models lately.
(They are not paying me to say that)
These guys are rocking it with small models lately.
(They are not paying me to say that)
- 10 replies
reacted to HannesVonEssen's post with 🔥 2 days ago
Post
2297
🏅 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
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
replied to their post 3 days ago
Thank you for appreciation!
I agree, this is not super impressive, there's room for improvement, and the team will definitely work together to make a better model soon!
You should try it out in the meantime, and let us know for any improvements you'd like.
posted an update 3 days ago
Post
101
🧩 PixelModel v6 is here! 155M parameters
Try it out on our demo (~15 seconds per image) 256x256 🎉
BenchLabs/Demo
Disclaimer: This model does not produce high quality (4k) and does not follow detailed prompts. Does not have negative prompt. 😔
How long did it take to train?
55 hours across two A100 gpu's 🔥
Model repo:
bench-labs/PixelModel-v6
@benchlabs
Try it out on our demo (~15 seconds per image) 256x256 🎉
BenchLabs/Demo
Disclaimer: This model does not produce high quality (4k) and does not follow detailed prompts. Does not have negative prompt. 😔
How long did it take to train?
55 hours across two A100 gpu's 🔥
Model repo:
bench-labs/PixelModel-v6
@benchlabs
- 3 replies
reacted to LH-Tech-AI's post with 🔥 5 days ago
Post
3392
Supra2-100M is out!
Go check it out:
- https://www.reddit.com/r/LocalLLaMA/comments/1velyl9/new_models_supra2100m_base_and_instruct_go_check/
- https://huggingface.co/SupraLabs/Supra2-100M
- SupraLabs/Supra2-100M-Instruct
Give us a like and a follow!!
HAVE FUN 🤗🔥🚀
more coming soon...
Go check it out:
- https://www.reddit.com/r/LocalLLaMA/comments/1velyl9/new_models_supra2100m_base_and_instruct_go_check/
- https://huggingface.co/SupraLabs/Supra2-100M
- SupraLabs/Supra2-100M-Instruct
Give us a like and a follow!!
HAVE FUN 🤗🔥🚀
more coming soon...
- 1 reply
reacted to DavidAU's post with 🔥 6 days ago
Post
8970
Qwen 3.6 27B - Fable Fusion 711 - Closed Source AI Performance Levels
1256 likes || 1.37 Million downloads || 32 quant repos || Multiple 3rd party performance verification.
The strongest Qwen 3.6 27B fine tune BASE ever.
It beats everyone - confirmed by 3rd party evaluation, multiple users, and in depth testing.
Q8 runs hotter and better than BF16 of the org Qwen 3.6 27B from Qwen.
And so does the 4 bit versions too.
GGUFS (MTP/Reg) and Several other quant types too:
DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF
SOURCE:
DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTP
(you can try it right in your browser at the source repo)
PS: 40B versions in testing, already SOTA levels beyond Qwen 3.6 27B.
arc/c arc/e boolq hswag obkqa piqa wino
Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF [instruct mode]
mxfp8 0.711,0.879,0.910,0.790,0.514,0.823,0.763
mxfp4 0.701,0.873,0.909,0.786,0.488,0.813,0.759
Qwen3.6-27B-Instruct: [base, non heretic]
mxfp8 0.647,0.803,0.910,0.773,0.450,0.806,0.742
Qwen3.6-35B-A3B-Instruct [base, non heretic]
mxfp8 0.581,0.757,0.892,0.751,0.428,0.803,0.688
1256 likes || 1.37 Million downloads || 32 quant repos || Multiple 3rd party performance verification.
The strongest Qwen 3.6 27B fine tune BASE ever.
It beats everyone - confirmed by 3rd party evaluation, multiple users, and in depth testing.
Q8 runs hotter and better than BF16 of the org Qwen 3.6 27B from Qwen.
And so does the 4 bit versions too.
GGUFS (MTP/Reg) and Several other quant types too:
DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF
SOURCE:
DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTP
(you can try it right in your browser at the source repo)
PS: 40B versions in testing, already SOTA levels beyond Qwen 3.6 27B.
arc/c arc/e boolq hswag obkqa piqa wino
Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF [instruct mode]
mxfp8 0.711,0.879,0.910,0.790,0.514,0.823,0.763
mxfp4 0.701,0.873,0.909,0.786,0.488,0.813,0.759
Qwen3.6-27B-Instruct: [base, non heretic]
mxfp8 0.647,0.803,0.910,0.773,0.450,0.806,0.742
Qwen3.6-35B-A3B-Instruct [base, non heretic]
mxfp8 0.581,0.757,0.892,0.751,0.428,0.803,0.688
- 5 replies
reacted to Enderchef's post with 👀 7 days ago
Post
2363
I've left Glint Research.
After a long time in Glint Research, an entire distributed training grid built free for them, and more, I've decided that I no longer want to be affiliated with Glint Research.
More updates will follow. Comments/questions are welcome.
While you're reading this, follow these orgs! Following takes just a few seconds, and can change someone's day.
AxiomicLabs
fromziro
SupraLabs
After a long time in Glint Research, an entire distributed training grid built free for them, and more, I've decided that I no longer want to be affiliated with Glint Research.
More updates will follow. Comments/questions are welcome.
While you're reading this, follow these orgs! Following takes just a few seconds, and can change someone's day.
- 2 replies
reacted to Banaxi-Tech's post with 🔥 14 days ago
Post
2866
We're excited to release BananaMind Base Bench 1.1 A new benchmark for base language models with 350 text-completion examples across seven categories. Models are scored using continuation likelihood and receive an Overall Elo score.
Initial results:
BananaMind-2-Medium: 1034
BananaMind-2-Mini: 974
Supra-50M-Base: 973
Supra-1.5-50M-Base-exp: 948
BananaMind-2-Nano: 910
The official script downloads the gated dataset directly from Hugging Face. The dataset is for benchmarking only and may not be used for model training.
BananaMind/BananaMind-Base-Bench-1.1
Initial results:
BananaMind-2-Medium: 1034
BananaMind-2-Mini: 974
Supra-50M-Base: 973
Supra-1.5-50M-Base-exp: 948
BananaMind-2-Nano: 910
The official script downloads the gated dataset directly from Hugging Face. The dataset is for benchmarking only and may not be used for model training.
BananaMind/BananaMind-Base-Bench-1.1
- 12 replies
reacted to sergiopaniego's post with 🤗 14 days ago
Post
1518
you can train DiffusionGemma (a block-diffusion LLM) in TRL! and we're sharing an example for it
TRL trainers are made to be easily extended and adapted to different real-world use cases.
in this one, with a single method overridden in SFTTrainer (compute_loss), you can train this model
> example: https://github.com/huggingface/trl/blob/main/examples/scripts/sft_diffusion_gemma.py
TRL trainers are made to be easily extended and adapted to different real-world use cases.
in this one, with a single method overridden in SFTTrainer (compute_loss), you can train this model
> example: https://github.com/huggingface/trl/blob/main/examples/scripts/sft_diffusion_gemma.py
reacted to badaoui's post with 🚀👍 15 days ago
Post
2329
432 GB of ultra-fast HBM4 and up to 23.3 TB/s of memory bandwidth on a single GPU 🤯.
Two weeks ago, we got early access to AMD's new Instinct MI455X, and our first goal was simple: make sure 🤗 Transformers works on day one.
Over the past few weeks, we worked closely with the AMD team to validate the platform, enable Flash Attention, add torchcodec support for multimodal models, and resolve issues uncovered during testing.
The result:
✅ 99.5% success rate across our 24 core Transformers model architectures - already on par with our daily CI on previous AMD and NVIDIA platforms.
The hardware is just as exciting. With 432 GB of HBM per GPU, our early capacity experiments showed more than 3× the concurrent long-context requests compared to MI300, thanks to the much larger KV cache capacity.
A huge thanks to the AMD team for the early access and the great collaboration!
Read the full blog 👇
https://huggingface.co/blog/badaoui/transformers-on-amd-mi455
Two weeks ago, we got early access to AMD's new Instinct MI455X, and our first goal was simple: make sure 🤗 Transformers works on day one.
Over the past few weeks, we worked closely with the AMD team to validate the platform, enable Flash Attention, add torchcodec support for multimodal models, and resolve issues uncovered during testing.
The result:
✅ 99.5% success rate across our 24 core Transformers model architectures - already on par with our daily CI on previous AMD and NVIDIA platforms.
The hardware is just as exciting. With 432 GB of HBM per GPU, our early capacity experiments showed more than 3× the concurrent long-context requests compared to MI300, thanks to the much larger KV cache capacity.
A huge thanks to the AMD team for the early access and the great collaboration!
Read the full blog 👇
https://huggingface.co/blog/badaoui/transformers-on-amd-mi455
- 1 reply
reacted to salma-remyx's post with 👀 16 days ago
Post
2912
Your coding agent is waiting on you to decide what to try next.
It doesn't originate that decision on its own.
What's usually missing is a way to generate that decision systematically, grounded in something more than the random paper that came across someone's feed that week.
Outrider starts from research with code and data behind it to scope a change applying the core method in your own codebase. A feature branch gets gated on your own evaluation methods before it reaches you in review.
The result is tied to what actually happened in your system, not to a model's read on its own output.
Here's what a code recommendation system looks like end to end.
It doesn't originate that decision on its own.
What's usually missing is a way to generate that decision systematically, grounded in something more than the random paper that came across someone's feed that week.
Outrider starts from research with code and data behind it to scope a change applying the core method in your own codebase. A feature branch gets gated on your own evaluation methods before it reaches you in review.
The result is tied to what actually happened in your system, not to a model's read on its own output.
Here's what a code recommendation system looks like end to end.
- 7 replies
replied to their post 17 days ago
hey, are you an ai agent?
reacted to FlameF0X's post with 🤗 17 days ago
Post
253
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
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